Posts in category: Amazon AWS

Amazon Q Business is a fully managed, generative AI-powered assistant that you can configure to answer questions, provide summaries, generate content, and complete tasks based on your enterprise data. Amazon Q Business offers over 40 built-in connectors to popular enterprise applications and document repositories, including Amazon Simple Storage Service (Amazon S3), Salesforce, Google Drive, Microsoft 365, ServiceNow, Gmail, Slack, Atlassian, and Zendesk and can help you create your generative AI solution with minimal configuration.

Nearly 100 thousand organizations use Slack to bring the right people together to securely collaborate with each other. A Slack workspace captures invaluable organizational knowledge in the form of the information that flows through it as the users communicate on it. Hence, it is valuable to make this knowledge quickly and securely available to the users.

In this post, we will demonstrate how to set up Slack connector for Amazon Q Business to sync communications from both public and private channels, reflective of user permissions. We will also guide you through the configurations needed on your Slack workspace. Additionally, you will learn how to configure the Amazon Q Business application and enable user authentication through AWS IAM Identity Center, which is a recommended service for managing a workforce’s access to AWS applications.

Data source overview

Amazon Q Business uses large language models (LLMs) to build a unified solution that connects multiple data sources. Typically, you’d need to use a natural language processing (NLP) technique called Retrieval Augmented Generation (RAG) for this. With RAG, generative AI enhances its responses by incorporating relevant information retrieved from a curated dataset. Amazon Q Business has a built-in managed RAG capability designed to reduce the undifferentiated heavy lifting involved in creating these systems. Typical of a RAG model, Amazon Q Business has two components: A retrieval component that retrieves relevant documents for the user query and a generation component that takes the query and the retrieved documents and then generates an answer to the query using an LLM.

A Slack workspace has multiple elements. It has public channels where workspace users can participate and private channels where only channel members can communicate with each other. Individuals can also directly communicate with each other in one-on-one conversations and in user groups. This communication is in the form of messages and threads of replies, with optional document attachments. Slack workspaces of active organizations are highly dynamic, with the content and collaboration evolving and growing in volume continuously.

The preceding figure shows the process flow of the solution. When you connect Amazon Q Business to a data source (in this case, Slack), what Amazon Q considers and crawls as a document varies by connector. For the Amazon Q Business Slack connector, each message, message attachment and channel post is considered a single document, However, Slack conversation threads that help you create organized discussions around specific messages are also considered and ingested as a single document, regardless of the number of participants or messages they contain.

Amazon Q Business crawls access control list (ACL) information attached to a document (user and group information) from your Slack instance. This information can be used to filter chat responses to the user’s document access level. The Slack connector supports token-based authentication. This could be a Slack bot user OAuth token or Slack user OAuth token. See the Slack connector overview to get the list of entities that are extracted, supported filters, sync modes, and file types.

User IDs (_user_id) exist in Slack on messages and channels where there are set access permissions. They are mapped from the user emails as the IDs in Slack.

To connect your data source connector to Amazon Q Business, you must give Amazon Q Business an IAM role that has the following permissions:

• Permission to access the BatchPutDocument and BatchDeleteDocument operations to ingest documents.
• Permission to access the User Store API operations to ingest user and group access control information from documents.
• Permission to access your AWS Secrets Manager secret to authenticate your data source connector instance.
• (Optional) If you’re using Amazon Virtual Private Cloud (Amazon VPC), permission to access your Amazon VPC.

Solution overview

In this solution, we will show you how to create a Slack workspace with users who perform various roles within the organization. We will then show you how to configure this workspace to define a set of scopes that are required by the Amazon Q Business Slack connector to index the user communication. This will be followed by the configuration of the Amazon Q Business application and a Slack data source. Based on the configuration, when the data source is synchronized, the connector either crawls and indexes the content from the workspace that was created on or before a specific date. The connector also collects and ingests ACL information for each indexed message and document. Thus, the search results of a query made by a user includes results only from those documents that the user is authorized to read.

Prerequisites

To build the Amazon Q Business connector for Slack, you need the following:

In Slack:

• Create a Slack bot user OAuth token or Slack user OAuth token. You can choose either token to connect Amazon Q Business to your Slack data source. See the Slack documentation on access tokens for more information.
• Note your Slack workspace team ID from your Slack workspace main page URL. For example, https://app.slack.com/client/T0123456789/... where T0123456789 is the team ID.
• Add the OAuth scopes and read permissions.

In your AWS account:

• Create an AWS Identity and Access Management (IAM) role for your data source and, if using the Amazon Q Business API, note the ARN of the IAM role.
• Store your Slack authentication credentials in an AWS Secrets Manager secret and, if using the Amazon Q Business API, note the ARN of the secret.
• Enable and configure an IAM Identity Center instance. Amazon Q Business integrates with IAM Identity Center as a gateway to manage user access to your Amazon Q Business application. We recommend enabling and pre-configuring an Identity Center instance before you begin to create your Amazon Q Business application. Identity Center is the recommended AWS service for managing human user access to AWS resources. Amazon Q Business supports both organization and account level Identity Center instances. See Setting up for Amazon Q Business for more information.

Configure your Slack workspace

You will create one user for each of the following roles: Administrator, Data scientist, Database administrator, Solutions architect and Generic.

To showcase the ACL propagation, you will create three public channels, #general, #customerwork, and #random, that any member can access including the Generic user. Also, one private channel, #anydepartment-project-private, that can be accessed only by the users arnav_desai, john_stiles, mary_major, and pat_candella.

To create a Slack app:

1. Navigate to the Slack API Your Apps page and choose Create New App.
   
2. Select From scratch. In the next screen, select the workspace to develop your app, and then choose Create an App.
   
3. Give the Slack app a name and select a workspace to develop your app in. Then choose Create App.
   
4. After you’ve created your app, select it and navigate to Features and choose OAuth & Permissions.
5. Scroll down to Scopes > User Token Scopes and set the OAuth scope based on the user token scopes in Prerequisites for connecting Amazon Q Business to Slack.
   

Note: You can configure two types of scopes in a Slack workspace:

1. Bot token scope: Only the messages to which it has been explicitly added are crawled by the bot token. It is employed to grant restricted access to specific messages only.
2. User token scope: Only the data shared with the member is accessible to the user token, which acts as a representative of a Slack user.

For this example, so you can search on the conversations between users, you will use the user token scope.

6. After the OAuth scope for yser token has been set up as described in the Slack prerequisites, scroll up to the section OAuth Tokens for your Workspace, and choose Install to Workspace, and then choose Allow.
7. This will generate a user OAuth token. Copy this token to use when configuring the Amazon Q Business Slack connector.
   

Configure the data source using the Amazon Q Business Slack connector

In this section, you will create an Amazon Q Business application using the console.

To create an Amazon Q Business application

1. In the AWS Management Console for Amazon Q Business, choose Create Application.
   
2. Enter an Application Name, such as my-slack-workspace. Leave the Service access as the default value, and select AWS IAM Identity Center for Access Management . Enter a new Tag value as required and choose Create to the Amazon Q Business Application.
   
3. Leave the default option of Use Native retriever selected for Retrievers, leave Enterprise as the Index provisioning and leave the default value of 1 as the Number of units. Each unit in Amazon Q Business index is 20,000 documents or 200 MB of extracted text (whichever comes first). Choose Next.
   
4. Scroll down the list of available connectors and select Slack and then choose Next.
   
   1. Enter a Data source name and a Description to identify your data source and then enter the Slack workspace team ID to connect with Amazon Q Business.
      
   2. In the Authentication section, select Create and add a new secret.
      
   3. On the dialog box that appears, enter a Secret name followed by the User OAuth Slack token that was copied from the Slack workspace.
      
   4. For the IAM role, select Create a new service role (Recommended).
      
   5. In Sync scope, choose the following:
      • For select type of content to crawl, select All channels.
      • Select an appropriate date for Select crawl start date.
      • Leave the default value selected for Maximum file size as 50.
      • You can include specific Messages, such as bot messages or archived messages to sync.
      • Additionally, you can include up to 100 patterns to include or exclude filenames, types, or file paths to sync.

      

   6. For Sync mode, leave Full sync selected and for the Sync run schedule, select Run on demand.
      
   7. Leave the field mapping as is and choose Add data source.
   8. On the next page, choose Next.
5. Add the five users you created earlier, who are a part of IAM Identity Center and the Slack workspace to the Amazon Q Business application. To add users to Identity Center, follow the instructions in Add users to your Identity Center directory. When done, choose Add groups and users and choose Assign.
   
6. When a user is added, each user is assigned the default Q Business Pro For more information on different pricing tiers, see the Amazon Q Business pricing page.
   
7. Choose Create application to finish creating the Amazon Q Business application.
8. After the application and the data source are created, select the data source and then choose Sync now to start syncing documents from your data source.
   
9. The sync process ingests the documents from your Slack workspace to your selections in the Slack connector configuration in Amazon Q Business. The following screenshot shows the results of a successful sync, indicated by the status of Completed.
   

Search with Amazon Q Business

Now, you’re ready to make a few queries in Amazon Q Business.

To search using Amazon Q Business:

1. Navigate to the Web experience settings tab and click on the Deployed URL.
2. For this demonstration, sign in as pat_candella who has the role of DB Admin.
   
3. Enter the password for pat_candella and choose Sign in
   
4. Upon successful sign-in, you will be signed in to Amazon Q Business.
   
5. In the Slack workspace, there is a public channel, the #customerwork channel that all users are members of. The #customerwork Slack channel is being used to communicate about an upcoming customer engagement, as shown in the following figure.
   
6. Post the first question to Amazon Q Business.

I am currently using Apache Kafka. Can you list high level steps involved in migration to Amazon MSK?

Note that the response includes citations that refer to the conversation as well as the content of the PDF that was attached to the conversation.

Security and privacy options with Slack data connector

Next, you will create a private channel called #anydepartment-project-private with four out of the five users—arnav_desai, john_stiles, mary_major and pat_candella—and verify that the messages exchanged in a private channel are not available to non-members like jane_doe. Note that after you create a new private channel, you need to manually re-run the sync on the data source.

The below screenshot shows the private slack channel with four out of five users and the slack conversation.

Testing security and privacy options with Slack data connector

1. While signed in as pat_candella, who is part of the private #anydepartment-project-private channel, execute the following query:

   What is Amazon Kendra and which API do I use to query a Kendra index?

   

2. Now, sign in as jane_doe, who is not a member of the #anydepartment-project-private channel and execute the same query.
   
3. Amazon Q Business prevents jane_doe from getting insights from information within the private channels that they aren’t part of, based on the synced ACL information.

Indexing aggregated Slack threads

Slack organizes conversations into threads, which can involve multiple users and messages. The Amazon Q Business Slack connector treats each thread as a single document, regardless of the number of participants or messages it contains. This approach allows Amazon Q Business to ingest entire conversation threads as individual units, maximizing the amount of data that can be processed within a single index unit. As a result, you can efficiently incorporate more comprehensive conversational context into your Amazon Q Business system.

The figure that follows shows a conversation between pat_candella and jane_doe that includes six messages in a thread. The Slack connector aggregates this message thread as a single message, thus maximizing the use of an index unit.

Because the conversation thread is aggregated as a single document within the Amazon Q Business index, you can ask questions that pertain to a single conversation thread as shown in the following figure.

Troubleshooting the sync process

• Why isn’t Amazon Q Business answering any of my questions?

If you aren’t getting answers to your questions from Amazon Q Business, verify the following:

• Permissions – Document ACLs indexed by Amazon Q Business may not allow you to query certain data entities as demonstrated in our example. If this is the case, please reach out to your Slack workspace administrator to make sure that your user has access to required documents and repeat the sync process.
• Data connector sync – A failed data source sync may prevent the documents from being indexed, meaning that Amazon Q Business would be unable to answer questions about the documents that failed to sync. Please refer to the official documentation to troubleshoot data source connectors.

• I’m receiving access errors on Amazon Q Business application. What causes this?

See Troubleshooting Amazon Q Business identity and access to diagnose and fix common issues that you might encounter when working with Amazon Q and IAM.

• How can I sync documents without ACLs?

Amazon Q Business supports crawling ACLs for document security by default. Turning off ACLs and identity crawling are no longer supported. If you want to index documents without ACLs, ensure that the documents are marked as public in your data source. Please refer to the official documentation, How Amazon Q Business connector for crawls Slack ACLs.

• My connector is unable to sync. How can I monitor data source sync progress?

Amazon Q Business provides visibility into the data sync operations. Learn more about this feature in the AWS Machine Learning blog.

Additionally, as the sync process runs, you can monitor progress or debug failures by monitoring the Amazon CloudWatch logs that can be accessed from the Details section of the Sync run history.

A sample query to determine which documents or messages were indexed from a specific slack channel, C12AB34578, and logStream of SYNC_RUN_HISTORY_REPORT/xxxxxxxxxxxxxxxxxxxxxxxx would look like the following:

fields LogLevel, DocumentId, DocumentTitle, CrawlAction, ConnectorDocumentStatus.Status as ConnectorDocumentStatus, ErrorMsg, CrawlStatus.Status as CrawlStatus, SyncStatus.Status as SyncStatus, IndexStatus.Status as IndexStatus, SourceUri, Acl, Metadata, HashedDocumentId, @timestamp

| filter @logStream like 'SYNC_RUN_HISTORY_REPORT/xxxxxxxxxxxxxxxxxxxxxxxx' and Metadata like /"stringValue":"C12AB34578"/

| sort @timestamp desc

| limit 10000

Choosing Run query displays the list of messages as the Amazon Q Business Index sync runs, as shown in the following figure.

Cleanup

To delete an Amazon Q Business application, you can use the console or the DeleteApplication API operation.

To delete an Amazon Q Business application using the console

1. Sign in to the Amazon Q Business console.
2. Select the respective the Amazon Q Business Application and choose
3. Choose Delete
4. In the dialog box that opens, enter Delete to confirm deletion, and then choose Delete.
5. You are returned to the service console while your application is deleted. When the deletion process is complete, the console displays a message confirming successful deletion.

To delete the IAM Identity Center instance, see Delete your IAM Identity Center instance.

Conclusion

This blog post provides a step-by-step guide on setting up the Slack connector for Amazon Q Business, enabling you to seamlessly integrate data from your Slack workspace. Moreover, we highlighted the importance of data privacy and security, demonstrating how the connector adheres to the ACLs within your Slack workspace. This feature helps ensure that private channel conversations remain confidential and inaccessible to individuals who aren’t members of those channels. By following these steps and understanding the built-in security measures, you can use the power of Amazon Q Business while maintaining the integrity and privacy of your Slack workspace.

To learn more about the Amazon Q Business connector for Slack, see Connecting Slack to Amazon Q Business. You can automate all the showcased console operations through Amazon Q Business API’s, the AWS CLI and other applicable AWS SDKs.

If you choose to converse with Amazon Q Business using Slack direct messages (DMs) to ask questions and get answers based on company data or to get help creating new content such as email drafts, summarize attached files, and perform tasks, see Deploy a Slack gateway for Amazon Q, your business expert for information about how to bring Amazon Q, your business expert, to users in Slack.

About the Authors

Akshara Shah is a Senior Solutions Architect at Amazon Web Services. She provides strategic technical guidance to help customers design and build cloud solutions. She is currently focused on machine learning and AI technologies.

Roshan Thomas is a Senior Solutions Architect at Amazon Web Services. He is based in Melbourne, Australia and works closely with enterprise customers to accelerate their journey in the cloud. He is passionate about technology and helping customers architect and build solutions on AWS.

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Amazon Lookout for Metrics is a fully managed service that uses machine learning (ML) to detect anomalies in virtually any time-series business or operational metrics—such as revenue performance, purchase transactions, and customer acquisition and retention rates—with no ML experience required. The service, which was launched in March 2021, predates several popular AWS offerings that have anomaly detection, such as Amazon OpenSearch, Amazon CloudWatch, AWS Glue Data Quality, Amazon Redshift ML, and Amazon QuickSight.

After careful consideration, we have made the decision to end support for Amazon Lookout for Metrics, effective October 10, 2025. In addition, as of today, new customer sign-ups are no longer available. Existing customers will be able to use the service as usual until October 10, 2025, when we will end support for Amazon Lookout for Metrics.

In this post, we provide an overview of the alternate AWS services that offer anomaly detection capabilities for customers to consider transitioning their workloads to.

AWS services with anomaly detection capabilities

We recommend customers use Amazon OpenSearch, Amazon CloudWatch, Amazon Redshift ML, Amazon QuickSight, or AWS Glue Data Quality services for their anomaly detection use cases as an alternative to Amazon Lookout for Metrics. These AWS services offer generally available, ML-powered anomaly detection capabilities that can be used out of the box without requiring any ML expertise. Following is a brief overview of each service.

Using Amazon OpenSearch for anomaly detection

Amazon OpenSearch Service features a highly performant, integrated anomaly detection engine that enables the real-time identification of anomalies in streaming data as well as in historical data. You can pair anomaly detection with built-in alerting in OpenSearch to send notifications when there is an anomaly. To start using OpenSearch for anomaly detection you first must index your data into OpenSearch, from there you can enable anomaly detection in OpenSearch Dashboards. To learn more, see the documentation.

Using Amazon CloudWatch for anomaly detection

Amazon CloudWatch supports creating anomaly detectors on specific Amazon CloudWatch Log Groups by applying statistical and ML algorithms to CloudWatch metrics. Anomaly detection alarms can be created based on a metric’s expected value. These types of alarms don’t have a static threshold for determining alarm state. Instead, they compare the metric’s value to the expected value based on the anomaly detection model. To start using CloudWatch anomaly detection, you first must ingest data into CloudWatch and then enable anomaly detection on the log group.

Using Amazon Redshift ML for anomaly detection

Amazon Redshift ML makes it easy to create, train, and apply machine learning models using familiar SQL commands in Amazon Redshift data warehouses. Anomaly detection can be done on your analytics data through Redshift ML by using the included XGBoost model type, local models, or remote models with Amazon SageMaker. With Redshift ML, you don’t have to be a machine learning expert and you pay only for the training cost of the SageMaker models. There are no additional costs to using Redshift ML for anomaly detection. To learn more, see the documentation.

Using Amazon QuickSight for anomaly detection

Amazon QuickSight is a fast, cloud-powered, business intelligence service that delivers insights to everyone in the organization. As a fully managed service, QuickSight lets customers create and publish interactive dashboards that include ML insights. QuickSight supports a highly performant, integrated anomaly detection engine that uses proven Amazon technology to continuously run ML-powered anomaly detection across millions of metrics to discover hidden trends and outliers in customers’ data. This tool allows customers to get deep insights that are often buried in the aggregates and not scalable with manual analysis. With ML-powered anomaly detection, customers can find outliers in their data without the need for manual analysis, custom development, or ML domain expertise. To learn more, see the documentation.

Using Amazon Glue Data Quality for anomaly detection

Data engineers and analysts can use AWS Glue Data Quality to measure and monitor their data. AWS Glue Data Quality uses a rule-based approach that works well for known data patterns and offers ML-based recommendations to help you get started. You can review the recommendations and augment rules from over 25 included data quality rules. To capture unanticipated, less obvious data patterns, you can enable anomaly detection. To use this feature, you can write rules or analyzers and then turn on anomaly detection in AWS Glue ETL. AWS Glue Data Quality collects statistics for columns specified in rules and analyzers, applies ML algorithms to detect anomalies, and generates visual observations explaining the detected issues. Customers can use recommended rules to capture the anomalous patterns and provide feedback to tune the ML model for more accurate detection. To learn more, see the blog post, watch the introductory video, or see the documentation.

Using Amazon SageMaker Canvas for anomaly detection (a beta feature)

The Amazon SageMaker Canvas team plans to provide support for anomaly detection use cases in Amazon SageMaker Canvas. We’ve created an AWS CloudFormation template-based solution to give customers early access to the underlying anomaly detection feature. Customers can use the CloudFormation template to bring up an application stack that receives time-series data from an Amazon Managed Streaming for Apache Kafka (Amazon MSK) streaming source and performs near-real-time anomaly detection in the streaming data. To learn more about the beta offering, see Anomaly detection in streaming time series data with online learning using Amazon Managed Service for Apache Flink.

Frequently asked questions

1. What is the cutoff point for current customers?

We created an allow list of account IDs that have used Amazon Lookout for Metrics in the last 30 days and have active Amazon Lookout for Metrics resources, including detectors, within the service. If you are an existing customer and are having difficulties using the service, please reach out to us via AWS Customer Support for help.

2. How will access change before the sunset date?

Current customers can do all the things they could previously. The only change is that non-current customers cannot create any new resources in Amazon Lookout for Metrics.

3. What happens to my Amazon Lookout for Metrics resources after the sunset date?

After October 10, 2025, all references to AWS Lookout for Metrics models and resources will be deleted from Amazon Lookout for Metrics. You will not be able to discover or access Amazon Lookout for Metrics from your AWS Management Console and applications that call the Amazon Lookout for Metrics API will no longer work.

4. Will I be billed for Amazon Lookout for Metrics resources remaining in my account after October 10, 2025?

Resources created by Amazon Lookout for Metrics internally will be deleted after October 10, 2025. Customers will be responsible for deleting the input data sources created by them, such as Amazon Simple Storage Service (Amazon S3) buckets, Amazon Redshift clusters, and so on.

5. How do I delete my Amazon Lookout for Metrics resources?
   • Open the Lookout for Metrics console Detectors
   • Choose the detector from the list.
   • Choose Delete.
   • Repeat these steps for every detector.

6. How can I export anomalies data before deleting the resources?

Anomalies data for each measure can be downloaded for a detector by using the Amazon Lookout for Metrics APIs for a particular detector. Exporting Anomalies explains how to connect to a detector, query for anomalies, and download them into a format for later use.

Conclusion

In this blog post, we have outlined methods to create anomaly detectors using alternates such as Amazon OpenSearch, Amazon CloudWatch, and a CloudFormation template-based solution.

Resource links:

• Anomaly detection using Amazon OpenSearch: Create an anomaly detector, configure the model, set up detector jobs, and observe the results using Amazon OpenSearch.
• Anomaly detection using Amazon CloudWatch: Explore Amazon CloudWatch anomaly detection and set it up using the AWS Management Console, the AWS Command Line Interface (AWS CLI), or AWS CloudFormation.
• Create a CloudWatch alarm based on anomaly detection: Create a CloudWatch alarm based on anomaly detection and modify or delete an anomaly detection model.
• Anomaly detection in AWS Glue Data Quality: Detect unanticipated issues with your data using powerful ML-based anomaly detection algorithms. Use AWS Glue Data Quality to understand the anomaly and provide feedback to tune the ML model for accurate detection.

About the Author

Nirmal Kumar is Sr. Product Manager for the Amazon SageMaker service. Committed to broadening access to AI/ML, he steers the development of no-code and low-code ML solutions. Outside work, he enjoys travelling and reading non-fiction.

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This post is co-written with Less Wright and Wei Feng from Meta

Pre-training large language models (LLMs) is the first step in developing powerful AI systems that can understand and generate human-like text. By exposing models to vast amounts of diverse data, pre-training lays the groundwork for LLMs to learn general language patterns, world knowledge, and reasoning capabilities. This foundational process enables LLMs to perform a wide range of tasks without task-specific training, making them highly versatile and adaptable. Pre-training is essential for building a strong base of knowledge, which can then be refined and specialized through fine-tuning, transfer learning, or few-shot learning approaches.

In this post, we collaborate with the team working on PyTorch at Meta to showcase how the torchtitan library accelerates and simplifies the pre-training of Meta Llama 3-like model architectures. We showcase the key features and capabilities of torchtitan such as FSDP2, torch.compile integration, and FP8 support that optimize the training efficiency. We pre-train a Meta Llama 3 8B model architecture using torchtitan on Amazon SageMaker on p5.48xlarge instances, each equipped with 8 Nvidia H100 GPUs. We demonstrate a 38.23% performance speedup in the training throughput compared to the baseline without applying the optimizations (as shown in the following figure). Amazon SageMaker Model Training reduces the time and cost to train and tune machine learning (ML) models at scale without the need to manage infrastructure. You can take advantage of the highest-performing ML compute infrastructure currently available, and SageMaker can automatically scale infrastructure up or down, from one to thousands of GPUs.

To learn more, you can find our complete code sample on GitHub.

Introduction to torchtitan

torchtitan is a reference architecture for large-scale LLM training using native PyTorch. It aims to showcase PyTorch’s latest distributed training features in a clean, minimal code base. The library is designed to be simple to understand, use, and extend for different training purposes, with minimal changes required to the model code when applying various parallel processing techniques.

torchtitan offers several key features, including FSDP2 with per-parameter sharding, tensor parallel processing, selective layer and operator activation checkpointing, and distributed checkpointing. It supports pre-training of Meta Llama 3-like and Llama 2-like model architectures of various sizes and includes configurations for multiple datasets. The library provides straightforward configuration through TOML files and offers performance monitoring through TensorBoard. In the following sections, we highlight some of the key features of torchtitan.

Transitioning from FSDP1 to FSDP2

FSDP1 and FSDP2 are two approaches to fully sharded data parallel training. FSDP1 uses flat-parameter sharding, which flattens all parameters to 1D, concatenates them into a single tensor, pads it, and then chunks it across workers. This method offers bounded padding and efficient unsharded storage, but might not always allow optimal sharding for individual parameters. FSDP2, on the other hand, represents sharded parameters as DTensors sharded on dim-0, handling each parameter individually. This approach enables easier manipulation of parameters, for example per-weight learning rate, communication-free sharded state dicts, and simpler meta-device initialization. The transition from FSDP1 to FSDP2 reflects a shift towards more flexible and efficient parameter handling in distributed training, addressing limitations of the flat-parameter approach while potentially introducing new optimization opportunities.

torchtitan support for torch.compile

torch.compile is a key feature in PyTorch that significantly boosts model performance with minimal code changes. Through its just-in-time (JIT) compilation, it analyzes and transforms PyTorch code into more efficient kernels. torchtitan supports torch.compile, which delivers substantial speedups, especially for large models and complex architectures, by using techniques like operator fusion, memory planning, and automatic kernel selection. This is enabled by setting compile = true in the model’s TOML configuration file.

torchtitan support for FP8 linear operations

torchtitan provides support for FP8 (8-bit floating point) computation that significantly reduces memory footprint and enhances performance in LLM training. FP8 has two formats, E4M3 and E5M2, each optimized for different aspects of training. E4M3 offers higher precision, making it ideal for forward propagation, whereas E5M2, with its larger dynamic range, is better suited for backpropagation. When operating at a lower precision, FP8 has no impact on model accuracy, which we demonstrate by convergence comparisons of the Meta Llama 3 8B pre-training at 2,000 steps. FP8 support on torchtitan is through the torchao library, and we enable FP8 by setting enable_float8_linear = true in the model’s TOML configuration file.

torchtitan support for FP8 all-gather

This feature enables efficient communication of FP8 tensors across multiple GPUs, significantly reducing network bandwidth compared to bfloat16 all-gather operations. FP8 all-gather performs float8 casting before the all-gather operation, reducing the message size. Key to its efficiency is the combined absolute maximum (AMAX) AllReduce, which calculates AMAX for all float8 parameters in a single operation after the optimizer step, avoiding multiple small all-reduces. Similar to FP8 support, this also has no impact on model accuracy, which we demonstrate by convergence comparisons of the Meta Llama 3 8B pre-training.

Pre-training Meta Llama 3 8B with torchtitan on Amazon SageMaker

SageMaker training jobs offer several key advantages that enhance the pre-training process of Meta Llama 3-like model architectures with torchtitan. It provides a fully managed environment that simplifies large-scale distributed training across multiple instances, which is crucial for efficiently pre-training LLMs. SageMaker supports custom containers, which allows seamless integration of the torchtitan library and its dependencies, so all necessary components are readily available.

The built-in distributed training capabilities of SageMaker streamline the setup of multi-GPU and multi-node jobs, reducing the complexity typically associated with such configurations. Additionally, SageMaker integrates with TensorBoard, enabling real-time monitoring and visualization of training metrics and providing valuable insights into the pre-training process. With these features, researchers and practitioners can focus more on model development and optimization rather than infrastructure management, ultimately accelerating the iterative process of creating and refining custom LLMs.

Solution overview

In the following sections, we walk you through how to prepare a custom image with the torchtitan library, then configure a training job estimator function to launch a Meta Llama 3 8B model pre-training with the c4 dataset (Colossal Clean Crawled Corpus) on SageMaker. The c4 dataset is a large-scale web text corpus that has been cleaned and filtered to remove low-quality content. It is frequently used for pre-training language models.

Prerequisites

Before you begin, make sure you have the following requirements in place:

• An AWS account.
• A SageMaker domain and Amazon SageMaker Studio For instructions to create these, refer to Quick setup to Amazon SageMaker.
• A Hugging Face access token so you can download the Meta Llama 3 models and tokenizer to use later.
• You need to request a quota increase of at least 1 ml.p5.48xlarge instance for training job usage on SageMaker.

Build the torchtitan custom image

SageMaker BYOC (Bring Your Own Container) allows you to use custom Docker containers to train and deploy ML models. Typically, SageMaker provides built-in algorithms and preconfigured environments for popular ML frameworks. However, there may be cases where you have unique or proprietary algorithms, dependencies, or specific requirements that aren’t available in the built-in options, necessitating custom containers. In this case, we need to use the nightly versions of torch, torchdata, and the torchao package to train with FP8 precision.

We use the Amazon SageMaker Studio Image Build convenience package, which offers a command line interface (CLI) to simplify the process of building custom container images directly from SageMaker Studio notebooks. This tool eliminates the need for manual setup of Docker build environments, streamlining the workflow for data scientists and developers. The CLI automatically manages the underlying AWS services required for image building, such as Amazon Simple Storage Service (Amazon S3), AWS CodeBuild, and Amazon Elastic Container Registry (Amazon ECR), allowing you to focus on your ML tasks rather than infrastructure setup. It offers a simple command interface, handles packaging of Dockerfiles and container code, and provides the resulting image URI for use in SageMaker training and hosting.

Before getting started, make sure your AWS Identity and Access Management (IAM) execution role has the required IAM permissions and policies to use the Image Build CLI. For more information, see Using the Amazon SageMaker Studio Image Build CLI to build container images from your Studio notebooks. We have provided the Jupyter notebook to build the custom container in the GitHub repo.

Complete the following steps to build the custom image:

1. Install the Image Build package with the following command:

! pip install sagemaker-studio-image-build

2. To extend the pre-built image, you can use the included deep learning libraries and settings without having to create an image from scratch:

FROM 763104351884.dkr.ecr.${REGION}.amazonaws.com/pytorch-training:2.3.0-gpu-py311-cu121-ubuntu20.04-sagemaker

3. Next, specify the libraries to install. You need the nightly versions of torch, torchdata, and the torchao libraries:

RUN pip install --pre torch --force-reinstall --index-url https://download.pytorch.org/whl/nightly/cu121

RUN pip install --pre torchdata --index-url https://download.pytorch.org/whl/nightly

#install torchtitan dependencies
RUN pip install --no-cache-dir 
datasets>=2.19.0 
tomli>=1.1.0 
tensorboard 
sentencepiece 
tiktoken 
blobfile 
tabulate

#install torchao package for FP8 support
RUN pip install --pre torchao --index-url https://download.pytorch.org/whl/nightly/cu121
#Display installed packages for reference
RUN pip freeze

4. Use the Image Build CLI to build and push the image to Amazon ECR:

!sm-docker build --repository torchtitan:latest . You’re now ready to use this image for pre-training models with torchtitan in SageMaker.

Prepare your dataset (optional)

By default, the torchtitan library uses the allenai/c4 “en” dataset in its training configuration. This is streamed directly during training using the HuggingFaceDataset class. However, you may want to pre-train the Meta Llama 3 models on your own dataset residing in Amazon S3. For this purpose, we have prepared a sample Jupyter notebook to download the allenai/c4 “en” dataset from the Hugging Face dataset hub to an S3 bucket. We use the SageMaker InputDataConfiguration to load the dataset to our training instances in the later section. You can download the dataset with a SageMaker processing job available in the sample Jupyter notebook.

Launch your training with torchtitan

Complete the following steps to launch your training:

1. Import the necessary SageMaker modules and retrieve your work environment details, such as AWS account ID and AWS Region. Make sure to upgrade the SageMaker SDK to the latest version. This might require a SageMaker Studio kernel restart.

%pip install --upgrade "sagemaker>=2.224"
%pip install sagemaker-experiments

import os
import boto3
import sagemaker
from sagemaker import get_execution_role
from sagemaker.pytorch import PyTorch

role = get_execution_role()
print(f"SageMaker Execution Role: {role}")

client = boto3.client("sts")
account = client.get_caller_identity()["Account"]
print(f"AWS account: {account}")

session = boto3.session.Session()
region = session.region_name
print(f"AWS region: {region}")

sm_boto_client = boto3.client("sagemaker")
sagemaker_session = sagemaker.session.Session(boto_session=session)

default_bucket = sagemaker_session.default_bucket()
print("Default bucket for this session: ", default_bucket)

2. Clone the torchtitan repository and prepare the training environment. Create a source directory and move the necessary dependencies from the torchtitan directory. This step makes sure you have all the required files for your training process.

git clone https://github.com/pytorch/torchtitan.git
mkdir torchtitan/src
!mv  torchtitan/torchtitan/ torchtitan/train_configs/ torchtitan/train.py  torchtitan/src/

3. Use the following command to download the Meta Llama 3 tokenizer, which is essential for preprocessing your dataset. Provide your Hugging Face token.

   python torchtitan/src/torchtitan/datasets/download_tokenizer.py --repo_id meta-llama/Meta-Llama-3-8B --tokenizer_path "original" --hf_token="YOUR_HF_TOKEN"

One of the key advantages of torchtitan is its straightforward configuration through TOML files. We modify the Meta Llama-3-8b TOML configuration file to enable monitoring and optimization features.

4. Enable TensorBoard profiling for better insights into the training process:

[metrics]
log_freq = 10
enable_tensorboard = true
save_tb_folder = "/opt/ml/output/tensorboard"

5. Enable torch.compile for improved performance:

compile = true

6. Enable FP8 for more efficient computations:

float8]
enable_float8_linear = true

7. Activate FP8 all-gather for optimized distributed training:

enable_fsdp_float8_all_gather= true
precompute_float8_dynamic_scale_for_fsdp = true

8. To monitor the training progress, set up TensorBoard output. This allows you to visualize the training metrics in real time, providing valuable insights into how the model is learning.

from sagemaker.debugger import TensorBoardOutputConfig

LOG_DIR="/opt/ml/output/tensorboard"
tensorboard_output_config = TensorBoardOutputConfig(
s3_output_path=f"s3://sagemaker-{region}-{account}/tensorboard/",
container_local_output_path=LOG_DIR
)

9. Set up the data channels for SageMaker training. Create TrainingInput objects that point to the preprocessed dataset in Amazon S3, so your model has access to the training data it needs.

#update the path below the s3 dataset path from running the previous Jupyter Notebook from Step 2
training_dataset_location = "<PATH-TO-DATASET>" 

s3_train_bucket = training_dataset_location

if s3_train_bucket != None:
   train = sagemaker.inputs.TrainingInput(s3_train_bucket, distribution="FullyReplicated", s3_data_type="S3Prefix")
   data_channels = {"train": train}

10. With all the pieces in place, you’re ready to create the SageMaker PyTorch estimator. This estimator encapsulates all the configurations, including the custom container, hyperparameters, and resource allocations.

import os

from time import gmtime, strftime

hyperparameters = {
   "config_file": "train_configs/llama3_8b.toml"
}
timestamp = strftime("%Y-%m-%d-%H-%M", gmtime())


estimator = PyTorch(
   base_job_name=f'llama3-8b-{timestamp}',
   entry_point="train.py",
   image_uri="<PATH-TO-IMAGE-URI>",
   source_dir=os.path.join(os.getcwd(), "src"),
   role=role,
   instance_type="ml.p5.48xlarge",
   volume_size=800,
   instance_count=4,
   hyperparameters=hyperparameters,
   use_spot_instances = False,
   sagemaker_session=sagemaker_session,
   tensorboard_output_config=tensorboard_output_config,
   distribution={
   'torch_distributed': {'enabled': True},
   },
  
)

11. Initiate the model training on SageMaker:

estimator.fit(inputs=data_channels)

Performance numbers

The following table summarizes the performance numbers for the various training runs with different optimizations.

The performance results show clear optimization progress in Meta Llama 3 8B pre-training. torch.compile() delivered an 10.67% speedup, and FP8 linear operations tripled this to 33%. Adding FP8 all-gather further increased the speedup to 38.23% over the baseline. This progression demonstrates how combining optimization strategies significantly enhances training efficiency.

The following figure illustrates the stepwise performance gains for Meta Llama 3 8B pre-training on torchtitan with the optimizations.

These optimizations didn’t affect the model’s training quality. The loss curves for all optimization levels, including the baseline, torch.compile(), FP8 linear, and FP8 all-gather configurations, remained consistent throughout the training process, as shown in the following figure.

The following table showcases the consistent loss value with the different configurations.

Clean up

After you complete your training experiments, clean up your resources to avoid unnecessary charges. You can start by deleting any unused SageMaker Studio resources. Next, remove the custom container image from Amazon ECR by deleting the repository you created. If you ran the optional step to use your own dataset, delete the S3 bucket where this data was stored.

Conclusion

In this post, we demonstrated how to efficiently pre-train Meta Llama 3 models using the torchtitan library on SageMaker. With torchtitan’s advanced optimizations, including torch.compile, FP8 linear operations, and FP8 all-gather, we achieved a 38.23% acceleration in Meta Llama 3 8B pre-training without compromising the model’s accuracy.

SageMaker simplified the large-scale training by offering seamless integration with custom containers, effortless scaling across multiple instances, built-in support for distributed training, and integration with TensorBoard for real-time monitoring.

Pre-training is a crucial step in developing powerful and adaptable LLMs that can effectively tackle a wide range of tasks and applications. By combining the latest PyTorch distributed training features in torchtitan with the scalability and flexibility of SageMaker, organizations can use their proprietary data and domain expertise to create robust and high-performance AI models. Get started by visiting the GitHub repository for the complete code example and optimize your LLM pre-training workflow.

Special thanks

Special thanks to Gokul Nadathur (Engineering Manager at Meta), Gal Oshri (Principal Product Manager Technical at AWS) and Janosch Woschitz (Sr. ML Solution Architect at AWS) for their support to the launch of this post.

About the Authors

Roy Allela is a Senior AI/ML Specialist Solutions Architect at AWS.He helps AWS customers—from small startups to large enterprises—train and deploy foundation models efficiently on AWS. He is passionate about computational optimization problems and improving the performance of AI workloads.

Kanwaljit Khurmi is a Principal Solutions Architect at Amazon Web Services. He works with AWS customers to provide guidance and technical assistance, helping them improve the value of their solutions when using AWS. Kanwaljit specializes in helping customers with containerized and machine learning applications.

Trevor Harvey is a Principal Specialist in Generative AI at Amazon Web Services (AWS) and an AWS Certified Solutions Architect – Professional. He serves as a voting member of the PyTorch Foundation Governing Board, where he contributes to the strategic advancement of open-source deep learning frameworks. At AWS, Trevor works with customers to design and implement machine learning solutions and leads go-to-market strategies for generative AI services.

Less Wright is an AI/Partner Engineer in PyTorch. He works on Triton/CUDA kernels (Accelerating Dequant with SplitK work decomposition); paged, streaming, and quantized optimizers; and PyTorch Distributed (PyTorch FSDP).

Wei Feng is a Software Engineer on the PyTorch distributed team. He has worked on float8 all-gather for FSDP2, TP (Tensor Parallel) in TorchTitan, and 4-bit quantization for distributed QLoRA in TorchTune. He is also a core maintainer of FSDP2.

Read More

Time series forecasting is a critical component in various industries for making informed decisions by predicting future values of time-dependent data. A time series is a sequence of data points recorded at regular time intervals, such as daily sales revenue, hourly temperature readings, or weekly stock market prices. These forecasts are pivotal for anticipating trends and future demands in areas such as product demand, financial markets, energy consumption, and many more.

However, creating accurate and reliable forecasts poses significant challenges because of factors such as seasonality, underlying trends, and external influences that can dramatically impact the data. Additionally, traditional forecasting models often require extensive domain knowledge and manual tuning, which can be time-consuming and complex.

In this blog post, we explore a comprehensive approach to time series forecasting using the Amazon SageMaker AutoMLV2 Software Development Kit (SDK). SageMaker AutoMLV2 is part of the SageMaker Autopilot suite, which automates the end-to-end machine learning workflow from data preparation to model deployment. Throughout this blog post, we will be talking about AutoML to indicate SageMaker Autopilot APIs, as well as Amazon SageMaker Canvas AutoML capabilities. We’ll walk through the data preparation process, explain the configuration of the time series forecasting model, detail the inference process, and highlight key aspects of the project. This methodology offers insights into effective strategies for forecasting future data points in a time series, using the power of machine learning without requiring deep expertise in model development. The code for this post can be found in the GitHub repo.

The following diagram depicts the basic AutoMLV2 APIs, all of which are relevant to this post. The diagram shows the workflow for building and deploying models using the AutoMLV2 API. In the training phase, CSV data is uploaded to Amazon S3, followed by the creation of an AutoML job, model creation, and checking for job completion. The deployment phase allows you to choose between real-time inference via an endpoint or batch inference using a scheduled transform job that stores results in S3.

1. Data preparation

The foundation of any machine learning project is data preparation. For this project, we used a synthetic dataset containing time series data of product sales across various locations, focusing on attributes such as product code, location code, timestamp, unit sales, and promotional information. The dataset can be found in an Amazon-owned, public Amazon Simple Storage Service (Amazon S3) dataset.

When preparing your CSV file for input into a SageMaker AutoML time series forecasting model, you must ensure that it includes at least three essential columns (as described in the SageMaker AutoML V2 documentation):

1. Item identifier attribute name: This column contains unique identifiers for each item or entity for which predictions are desired. Each identifier distinguishes the individual data series within the dataset. For example, if you’re forecasting sales for multiple products, each product would have a unique identifier.
2. Target attribute name: This column represents the numerical values that you want to forecast. These could be sales figures, stock prices, energy usage amounts, and so on. It’s crucial that the data in this column is numeric because the forecasting models predict quantitative outcomes.
3. Timestamp attribute name: This column indicates the specific times when the observations were recorded. The timestamp is essential for analyzing the data in a chronological context, which is fundamental to time series forecasting. The timestamps should be in a consistent and appropriate format that reflects the regularity of your data (for example, daily or hourly).

All other columns in the dataset are optional and can be used to include additional time-series related information or metadata about each item. Therefore, your CSV file should have columns named according to the preceding attributes (item identifier, target, and timestamp) as well as any other columns needed to support your use case For instance, if your dataset is about forecasting product demand, your CSV might look something like this:

• Product_ID (item identifier): Unique product identifiers.
• Sales (target): Historical sales data to be forecasted.
• Date (timestamp): The dates on which sales data was recorded.

The process of splitting the training and test data in this project uses a methodical and time-aware approach to ensure that the integrity of the time series data is maintained. Here’s a detailed overview of the process:

Ensuring timestamp integrity

The first step involves converting the timestamp column of the input dataset to a datetime format using pd.to_datetime. This conversion is crucial for sorting the data chronologically in subsequent steps and for ensuring that operations on the timestamp column are consistent and accurate.

Sorting the data

The sorted dataset is critical for time series forecasting, because it ensures that data is processed in the correct temporal order. The input_data DataFrame is sorted based on three columns: product_code, location_code, and timestamp. This multi-level sort guarantees that the data is organized first by product and location, and then chronologically within each product-location grouping. This organization is essential for the logical partitioning of data into training and test sets based on time.

Splitting into training and test sets

The splitting mechanism is designed to handle each combination of product_code and location_code separately, respecting the unique temporal patterns of each product-location pair. For each group:

• The initial test set is determined by selecting the last eight timestamps (yellow + green below). This subset represents the most recent data points that are candidates for testing the model’s forecasting ability.
• The final test set is refined by removing the last four timestamps from the initial test set, resulting in a test dataset that includes the four timestamps immediately preceding the latest data (green below). This strategy ensures the test set is representative of the near-future periods the model is expected to predict, while also leaving out the most recent data to simulate a realistic forecasting scenario.
• The training set comprises the remaining data points, excluding the last eight timestamps (blue below). This ensures the model is trained on historical data that precedes the test period, avoiding any data leakage and ensuring that the model learns from genuinely past observations.

This process is visualized in the following figure with an arbitrary value on the Y axis and the days of February on the X axis.

The test dataset is used to evaluate the performance of the trained model and compute various loss metrics, such as mean absolute error (MAE) and root-mean-squared error (RMSE). These metrics quantify the model’s accuracy in forecasting the actual values in the test set, providing a clear indication of the model’s quality and its ability to make accurate predictions. The evaluation process is detailed in the “Inference: Batch, real-time, and asynchronous” section, where we discuss the comprehensive approach to model evaluation and conditional model registration based on the computed metrics.

Creating and saving the datasets

After the data for each product-location group is categorized into training and test sets, the subsets are aggregated into comprehensive training and test DataFrames using pd.concat. This aggregation step combines the individual DataFrames stored in train_dfs and test_dfs lists into two unified DataFrames:

• train_df for training data
• test_df for testing data

Finally, the DataFrames are saved to CSV files (train.csv for training data and test.csv for test data), making them accessible for model training and evaluation processes. This saving step not only facilitates a clear separation of data for modelling purposes but also enables reproducibility and sharing of the prepared datasets.

Summary

This data preparation strategy meticulously respects the chronological nature of time series data and ensures that the training and test sets are appropriately aligned with real-world forecasting scenarios. By splitting the data based on the last known timestamps and carefully excluding the most recent periods from the training set, the approach mimics the challenge of predicting future values based on past observations, thereby setting the stage for a robust evaluation of the forecasting model’s performance.

2. Training a model with AutoMLV2

SageMaker AutoMLV2 reduces the resources needed to train, tune, and deploy machine learning models by automating the heavy lifting involved in model development. It provides a straightforward way to create high-quality models tailored to your specific problem type, be it classification, regression, or forecasting, among others. In this section, we delve into the steps to train a time series forecasting model with AutoMLV2.

Step 1: Define the tine series forecasting configuration

The first step involves defining the problem configuration. This configuration guides AutoMLV2 in understanding the nature of your problem and the type of solution it should seek, whether it involves classification, regression, time-series classification, computer vision, natural language processing, or fine-tuning of large language models. This versatility is crucial because it allows AutoMLV2 to adapt its approach based on the specific requirements and complexities of the task at hand. For time series forecasting, the configuration includes details such as the frequency of forecasts, the horizon over which predictions are needed, and any specific quantiles or probabilistic forecasts. Configuring the AutoMLV2 job for time series forecasting involves specifying parameters that would best use the historical sales data to predict future sales.

The AutoMLTimeSeriesForecastingConfig is a configuration object in the SageMaker AutoMLV2 SDK designed specifically for setting up time series forecasting tasks. Each argument provided to this configuration object tailors the AutoML job to the specifics of your time series data and the forecasting objectives.

time_series_config = AutoMLTimeSeriesForecastingConfig(
    forecast_frequency='W',
    forecast_horizon=4,
    item_identifier_attribute_name='product_code',
    target_attribute_name='unit_sales',
    timestamp_attribute_name='timestamp',
    ...
)

The following is a detailed explanation of each configuration argument used in your time series configuration:

• forecast_frequency
  • Description: Specifies how often predictions should be made.
  • Value ‘W’: Indicates that forecasts are expected on a weekly basis. The model will be trained to understand and predict data as a sequence of weekly observations. Valid intervals are an integer followed by Y (year), M (month), W (week), D (day), H (hour), and min (minute). For example, 1D indicates every day and 15min indicates every 15 minutes. The value of a frequency must not overlap with the next larger frequency. For example, you must use a frequency of 1H instead of 60min.

• forecast_horizon
  • Description: Defines the number of future time-steps the model should predict.
  • Value 4: The model will forecast four time-steps into the future. Given the weekly frequency, this means the model will predict the next four weeks of data from the last known data point.

• forecast_quantiles
  • Description: Specifies the quantiles at which to generate probabilistic forecasts.
  • Values [p50,p60,p70,p80,p90]: These quantiles represent the 50th, 60th, 70th, 80th, and 90th percentiles of the forecast distribution, providing a range of possible outcomes and capturing forecast uncertainty. For instance, the p50 quantile (median) might be used as a central forecast, while the p90 quantile provides a higher-end forecast, where 90% of the actual data is expected to fall below the forecast, accounting for potential variability.

• filling
  • Description: Defines how missing data should be handled before training; specifying filling strategies for different scenarios and columns.
  • Value filling_config: This should be a dictionary detailing how to fill missing values in your dataset, such as filling missing promotional data with zeros or specific columns with predefined values. This ensures the model has a complete dataset to learn from, improving its ability to make accurate forecasts.

• item_identifier_attribute_name
  • Description: Specifies the column that uniquely identifies each time series in the dataset.
  • Value ’product_code’: This setting indicates that each unique product code represents a distinct time series. The model will treat data for each product code as a separate forecasting problem.

• target_attribute_name
  • Description: The name of the column in your dataset that contains the values you want to predict.
  • Value unit_sales: Designates the unit_sales column as the target variable for forecasts, meaning the model will be trained to predict future sales figures.

• timestamp_attribute_name
  • Description: The name of the column indicating the time point for each observation.
  • Value ‘timestamp’: Specifies that the timestamp column contains the temporal information necessary for modeling the time series.

• grouping_attribute_names
  • Description: A list of column names that, in combination with the item identifier, can be used to create composite keys for forecasting.
  • Value [‘location_code’]: This setting means that forecasts will be generated for each combination of product_code and location_code. It allows the model to account for location-specific trends and patterns in sales data.

The configuration provided instructs the SageMaker AutoML to train a model capable of weekly sales forecasts for each product and location, accounting for uncertainty with quantile forecasts, handling missing data, and recognizing each product-location pair as a unique series. This detailed setup aims to optimize the forecasting model’s relevance and accuracy for your specific business context and data characteristics.

Step 2: Initialize the AutoMLV2 job

Next, initialize the AutoMLV2 job by specifying the problem configuration, the AWS role with permissions, the SageMaker session, a base job name for identification, and the output path where the model artifacts will be stored.

automl_sm_job = AutoMLV2(
    problem_config=time_series_config,
    role=role,
    sagemaker_session=sagemaker_session,
    base_job_name='time-series-forecasting-job',
    output_path=f's3://{bucket}/{prefix}/output'
)

Step 3: Fit the model

To start the training process, call the fit method on your AutoMLV2 job object. This method requires specifying the input data’s location in Amazon S3 and whether SageMaker should wait for the job to complete before proceeding further. During this step, AutoMLV2 will automatically pre-process your data, select algorithms, train multiple models, and tune them to find the best solution.

automl_sm_job.fit(
    inputs=[AutoMLDataChannel(s3_data_type='S3Prefix', s3_uri=train_uri, channel_type='training')],
    wait=True,
    logs=True
)

Please note that model fitting may take several hours, depending on the size of your dataset and compute budget. A larger compute budget allows for more powerful instance types, which can accelerate the training process. In this situation, provided you’re not running this code as part of the provided SageMaker notebook (which handles the order of code cell processing correctly), you will need to implement some custom code that monitors the training status before retrieving and deploying the best model.

3. Deploying a model with AutoMLV2

Deploying a machine learning model into production is a critical step in your machine learning workflow, enabling your applications to make predictions from new data. SageMaker AutoMLV2 not only helps build and tune your models but also provides a seamless deployment experience. In this section, we’ll guide you through deploying your best model from an AutoMLV2 job as a fully managed endpoint in SageMaker.

Step 1: Identify the best model and extract name

After your AutoMLV2 job completes, the first step in the deployment process is to identify the best performing model, also known as the best candidate. This can be achieved by using the best_candidate method of your AutoML job object. You can either use this method immediately after fitting the AutoML job or specify the job name explicitly if you’re operating on a previously completed AutoML job.

# Option 1: Directly after fitting the AutoML job
best_candidate = automl_sm_job.best_candidate()

# Option 2: Specifying the job name directly
best_candidate = automl_sm_job.best_candidate(job_name='your-auto-ml-job-name')

best_candidate_name = best_candidate['CandidateName']

Step 2: Create a SageMaker model

Before deploying, create a SageMaker model from the best candidate. This model acts as a container for the artifacts and metadata necessary to serve predictions. Use the create_model method of the AutoML job object to complete this step.

endpoint_name = f"ep-{best_candidate_name}-automl-ts"

# Create a SageMaker model from the best candidate
automl_sm_model = automl_sm_job.create_model(name=best_candidate_name, candidate=best_candidate)

4. Inference: Batch, real-time, and asynchronous

For deploying the trained model, we explore batch, real-time, and asynchronous inference methods to cater to different use cases.

The following figure is a decision tree to help you decide what type of endpoint to use. The diagram outlines a decision-making process for selecting between batch, asynchronous, or real-time inference endpoints. Starting with the need for immediate responses, it guides you through considerations like the size of the payload and the computational complexity of the model. Depending on these factors, you can choose a faster option with lower computational requirements or a slower batch process for larger datasets.

Batch inference using SageMaker pipelines

• Usage: Ideal for generating forecasts in bulk, such as monthly sales predictions across all products and locations.
• Process: We used SageMaker’s batch transform feature to process a large dataset of historical sales data, outputting forecasts for the specified horizon.

The inference pipeline used for batch inference demonstrates a comprehensive approach to deploying, evaluating, and conditionally registering a machine learning model for time series forecasting using SageMaker. This pipeline is structured to ensure a seamless flow from data preprocessing, through model inference, to post-inference evaluation and conditional model registration. Here’s a detailed breakdown of its construction:

• Batch tranform step
  • Transformer Initialization: A Transformer object is created, specifying the model to use for batch inference, the compute resources to allocate, and the output path for the results.
  • Transform step creation: This step invokes the transformer to perform batch inference on the specified input data. The step is configured to handle data in CSV format, a common choice for structured time series data.

• Evaluation step
  • Processor setup: Initializes an SKLearn processor with the specified role, framework version, instance count, and type. This processor is used for the evaluation of the model’s performance.
  • Evaluation processing: Configures the processing step to use the SKLearn processor, taking the batch transform output and test data as inputs. The processing script (evaluation.py) is specified here, which will compute evaluation metrics based on the model’s predictions and the true labels.
  • Evaluation strategy: We adopted a comprehensive evaluation approach, using metrics like mean absolute error (MAE) and root-means squared error (RMSE) to quantify the model’s accuracy and adjusting the forecasting configuration based on these insights.
  • Outputs and property files: The evaluation step produces an output file (evaluation_metrics.json) that contains the computed metrics. This file is stored in Amazon S3 and registered as a property file for later access in the pipeline.

• Conditional model registration
  • Model metrics setup: Defines the model metrics to be associated with the model package, including statistics and explainability reports sourced from specified Amazon S3 URIs.
  • Model registration: Prepares for model registration by specifying content types, inference and transform instance types, model package group name, approval status, and model metrics.
  • Conditional registration step: Implements a condition based on the evaluation metrics (for example, MAE). If the condition (for example, MAE is greater than or equal to threshold) is met, the model is registered; otherwise, the pipeline concludes without model registration.

• Pipeline creation and runtime
  • Pipeline definition: Assembles the pipeline by naming it and specifying the sequence of steps to run: batch transform, evaluation, and conditional registration.
  • Pipeline upserting and runtime: The pipeline.upsert method is called to create or update the pipeline based on the provided definition, and pipeline.start() runs the pipeline.

The following figure is an example of the SageMaker Pipeline directed acyclic graph (DAG).

This pipeline effectively integrates several stages of the machine learning lifecycle into a cohesive workflow, showcasing how Amazon SageMaker can be used to automate the process of model deployment, evaluation, and conditional registration based on performance metrics. By encapsulating these steps within a single pipeline, the approach enhances efficiency, ensures consistency in model evaluation, and streamlines the model registration process—all while maintaining the flexibility to adapt to different models and evaluation criteria.

Inferencing with Amazon SageMaker Endpoint in (near) real-time

But what if you want to run inference in real-time or asynchronously? SageMaker real-time endpoint inference offers the capability to deliver immediate predictions from deployed machine learning models, crucial for scenarios demanding quick decision making. When an application sends a request to a SageMaker real-time endpoint, it processes the data in real time and returns the prediction almost immediately. This setup is optimal for use cases that require near-instant responses, such as personalized content delivery, immediate fraud detection, and live anomaly detection.

• Usage: Suited for on-demand forecasts, such as predicting next week’s sales for a specific product at a particular location.
• Process: We deployed the model as a SageMaker endpoint, allowing us to make real-time predictions by sending requests with the required input data.

Deployment involves specifying the number of instances and the instance type to serve predictions. This step creates an HTTPS endpoint that your applications can invoke to perform real-time predictions.

# Deploy the model to a SageMaker endpoint
predictor = automl_sm_model.deploy(initial_instance_count=1, endpoint_name=endpoint_name, instance_type='ml.m5.xlarge')

The deployment process is asynchronous, and SageMaker takes care of provisioning the necessary infrastructure, deploying your model, and ensuring the endpoint’s availability and scalability. After the model is deployed, your applications can start sending prediction requests to the endpoint URL provided by SageMaker.

While real-time inference is suitable for many use cases, there are scenarios where a slightly relaxed latency requirement can be beneficial. SageMaker Asynchronous Inference provides a queue-based system that efficiently handles inference requests, scaling resources as needed to maintain performance. This approach is particularly useful for applications that require processing of larger datasets or complex models, where the immediate response is not as critical.

• Usage: Examples include generating detailed reports from large datasets, performing complex calculations that require significant computational time, or processing high-resolution images or lengthy audio files. This flexibility makes it a complementary option to real-time inference, especially for businesses that face fluctuating demand and seek to maintain a balance between performance and cost.
• Process: The process of using asynchronous inference is straightforward yet powerful. Users submit their inference requests to a queue, from which SageMaker processes them sequentially. This queue-based system allows SageMaker to efficiently manage and scale resources according to the current workload, ensuring that each inference request is handled as promptly as possible.

Clean up

To avoid incurring unnecessary charges and to tidy up resources after completing the experiments or running the demos described in this post, follow these steps to delete all deployed resources:

1. Delete the SageMaker endpoints: To delete any deployed real-time or asynchronous endpoints, use the SageMaker console or the AWS SDK. This step is crucial as endpoints can accrue significant charges if left running.
2. Delete the SageMaker Pipeline: If you have set up a SageMaker Pipeline, delete it to ensure that there are no residual executions that might incur costs.
3. Delete S3 artifacts: Remove all artifacts stored in your S3 buckets that were used for training, storing model artifacts, or logging. Ensure you delete only the resources related to this project to avoid data loss.
4. Clean up any additional resources: Depending on your specific implementation and additional setup modifications, there may be other resources to consider, such as roles or logs. Check your AWS Management Console for any resources that were created and delete them if they are no longer needed.

Conclusion

This post illustrates the effectiveness of Amazon SageMaker AutoMLV2 for time series forecasting. By carefully preparing the data, thoughtfully configuring the model, and using both batch and real-time inference, we demonstrated a robust methodology for predicting future sales. This approach not only saves time and resources but also empowers businesses to make data-driven decisions with confidence.

If you’re inspired by the possibilities of time series forecasting and want to experiment further, consider exploring the SageMaker Canvas UI. SageMaker Canvas provides a user-friendly interface that simplifies the process of building and deploying machine learning models, even if you don’t have extensive coding experience.

Visit the SageMaker Canvas page to learn more about its capabilities and how it can help you streamline your forecasting projects. Begin your journey towards more intuitive and accessible machine learning solutions today!

About the Authors

Nick McCarthy is a Senior Machine Learning Engineer at AWS, based in London. He has worked with AWS clients across various industries including healthcare, finance, sports, telecoms and energy to accelerate their business outcomes through the use of AI/ML. Outside of work he loves to spend time travelling, trying new cuisines and reading about science and technology. Nick has a Bachelors degree in Astrophysics and a Masters degree in Machine Learning.

Davide Gallitelli is a Senior Specialist Solutions Architect for AI/ML in the EMEA region. He is based in Brussels and works closely with customers throughout Benelux. He has been a developer since he was very young, starting to code at the age of 7. He started learning AI/ML at university, and has fallen in love with it since then.

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In this post, we present a solution that harnesses the power of generative AI to streamline the user onboarding process for financial services through a digital assistant. Onboarding new customers in the banking industry is a crucial step in the customer journey, involving a series of activities designed to fulfill know your customer (KYC) requirements, conduct necessary verifications, and introduce them to the bank’s products or services. Traditionally, customer onboarding has been a tedious and heavily manual process. Our solution provides practical guidance on addressing this challenge by using a generative AI assistant on AWS.

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API, along with a broad set of capabilities you need to build generative AI applications with security, privacy, and responsible AI. Using Anthropic’s Claude 3.5 Sonnet on Amazon Bedrock, we build a digital assistant that automates document processing, identity verifications, and engages customers through conversational interactions. As a result, customers can be onboarded in a matter of minutes through secure, automated workflows. In this post we provide you a solution and the accompanying code that banks can use to dramatically enhance the customer experience and establish a strong customer relationship from the outset.

Challenges with traditional onboarding

The traditional onboarding process for banks faces challenges in the current digital landscape because many institutions don’t have fully automated account-opening systems. While customers in other sectors have access to intelligent assistants, those in banking often encounter legacy processes. As the financial services industry adapts to changing consumer expectations, there’s a need to address the demand for instant and 24/7 availability of services.

The challenges associated with the manual onboarding process include but aren’t limited to, the following:

• Time-consuming paperwork – New customers are asked to manually fill out extensive paperwork including account opening forms, disclosures, and so on. Reviewing physical documents also takes up valuable staff time. This lengthy paperwork process can result in slow onboarding and a poor customer experience.
• Security risks – Paper documents and in-person ID verification lack security compared to digital processes because of their susceptibility to tampering, loss, and lack of traceability. For example, there’s a greater risk of identity theft and fraud with physical documents, because they can be altered or misplaced without leaving an audit trail.
• Accessibility issues – Requiring in-person account opening at branches can create accessibility challenges for many customers, including senior citizens and disabled individuals.
• Limited service hours – The account opening process is available only during branch operating hours, which limits the timeframe when customers can complete the onboarding process. This constraint impacts the flexibility for customers to initiate account opening at their preferred time.
• High costs – Manual paperwork processing and in-person verification are labor-intensive tasks that require significant staff time and resources, leading to high operational costs.

AI-powered services enable automated, secure, and compliant processes for self-service account opening. Providing onboarding experiences aligned with current digital standards might offer a competitive edge for banks in the future.

Solution overview

The solution allows users to open bank accounts remotely through a conversational interface, eliminating the need to visit a physical branch. We created a digital assistant named Penny to guide users through the process, including uploading KYC documents and facilitating identity verification using document scanning and facial recognition. The approach uses Retrieval Augmented Generation (RAG), which combines text generation capabilities with database querying to provide contextually relevant responses to customer inquiries. Implementing digital onboarding reduces the accessibility barriers present in traditional manual account opening processes. The code for this solution is available in a GitHub repository.

The brain of our application is a custom LangChain Agent. When a user wants to open a new bank account, the agent will help them complete the onboarding process using preconfigured stages corresponding to each onboarding step. Each stage might use a LangChain tool, allowing for the automation and orchestration of onboarding. These tools call on AWS service APIs for the required functionality.

The following figure represents the high-level architecture of the proposed solution.

The flow of the application is as follows:

1. Users access the frontend website hosted within AWS Amplify. AWS Amplify is an end-to-end solution that enables frontend web developers to build and deploy secure, scalable full stack applications.
2. The website invokes an Amazon CloudFront endpoint to interact with the digital assistant, Penny, which is containerized and deployed in AWS Fargate. Fargate is a serverless compute engine for containers that manages and scales your containers for you, compatible with Amazon Elastic Container Service (Amazon ECS).
3. The digital assistant uses a custom LangChain agent to answer questions on the bank’s products and services and orchestrate the onboarding flow.
4. If the user asks a general question related to the bank’s products or service, the agent will use a custom LangChain tool called ProductSearch. This tool uses Amazon Kendra linked with an Amazon Simple Storage Service (Amason S3) data source that contains the bank’s data. Amazon Kendra is an intelligent enterprise search service powered by machine learning that enables companies to index and search content across their document stores.
5. If the user indicates that they want to open a new account, the agent will prompt the user for their email. After the user responds, the application will invoke a custom LangChain tool called EmailValidation. This tools checks if there is an existing account in the bank’s Amazon DynamoDB database, by calling an endpoint deployed in Amazon API Gateway.
6. After the email validation, KYC information is gathered, such as first and last name. Then, the user is prompted for an identity document, which is uploaded to Amazon S3.
7. The agent will invoke a custom LangChain tool called IDVerification. This tool checks if the user details entered during the session match the ID by calling an endpoint deployed in Amazon API Gateway. The details are verified by extracting the document text using Amazon Textract, a machine learning (ML) service that automatically extracts text, handwriting, layout elements, and data from scanned documents.
8. After the ID verification, the user is asked for a selfie. The image is uploaded to Amazon S3. Then, the agent will invoke a custom LangChain tool called SelfieVerification. This tool checks if the uploaded selfie matches the face on the ID by calling an endpoint deployed in API Gateway. The face match is detected using Amazon Rekognition, which offers pre-trained and customizable computer vision (CV) capabilities to extract information and insights from your images and videos.
9. After the face verification is successful, the agent will use a custom LangChain tool called SaveData. This tool creates a new account in the bank’s DynamoDB database by calling an endpoint deployed in API Gateway.
10. The user is notified that their new account has been created successfully, using Amazon Simple Email Service (Amazon SES).

Prompt design for agent orchestration

Now, let’s take a look at how we give our digital assistant, Penny, the capability to handle onboarding for financial services. The key is the prompt engineering for the custom LangChain agent. This has been specified in PennyAgent.py. This prompt includes onboarding stages and relevant LangChain tools that the agent might need to complete the onboarding steps.

To begin, we provide the agent with a name, role and company.

AGENT_TOOLS_PROMPT = """
Never forget your name is {assistant_name}. You work as a {assistant_role}.
You work at company named {bank_name}

Next, we define the various stages of onboarding and specify the respective tools and expected responses. Having stages in a sequential and structured format while also providing awareness of all possible stages helps the agent determine the onboarding stage with accuracy.

<STAGES>

These are the stages:

Introduction or greeting:  When conversation history is empty, choose stage 1
Response: Start the conversation with a greeting. Say that you can help with {bank_name} related questions or open a bank account for them. Do this only during the start of the conversation.
Tool: 
    
General Banking Questions: Customer asks general questions about AnyBank
Response: Use ProductSearch tool to get the relevant information and answer the question like a banking assistant. Never assume anything.
Tool: ProductSearch
    
Account Open 1: Customer has requested to open an account.
Response: Customer has requested to open an account. Now, respond with a question asking for the customer's email address only to get them started with onboarding. We need the email address to start the process.
Tool:
    
Account Open 2: User provided their email.
Response: Take the email and validate it using a EmailValidation tool. If it is valid and there is no existing account with the email, ask for account type: either CHEQUING or SAVINGS. If it is invalid or there is an existing account with the email, the user must try again. 
Tool: EmailValidation
    
Account Open 3: User provided which account type to open.
Response: Ask the user for their first name
Tool: 

Account Open 4: User provided first name.
Response: Ask the user for their last name
Tool: 

Account Open 5: User provided last name.
Response: Ask the user to upload an identity document.
Tool:
    
Account Open 6: Penny asked for identity document and then System notified that a new file has been uploaded
Response: Take the identity file name and verify it using the IDVerification tool. If the verification is unsuccessful, ask the user to try again. 
Tool: IDVerification
    
Account Open 7: The ID document is valid. 
Response: Ask the user to upload their selfie to compare their face to the ID.
Tool:
    
Account Open 8: Penny asked user for their selfie and then "System notified that a file has been uploaded. "
Response: Take the "selfie" file name and verify it using the SelfieVerification tool. If there is no face match, ask the user to try again.
Tool: SelfieVerification: Use this tool to verify the user selfie and compare faces. 
    
Account Open 9: Face match verified
Response: Give the summary of the all the information you collected and ask user to confirm. 
Tool:
        
Account Open 10: Confirmation
Response: Save the user data for future reference using SaveData tool. Upon saving the data, let the user know that they will receive an email confirmation of the bank account opening.
Tool: SaveData

We append the tools, their descriptions, and their response formats to the prompt. When calling on a specific tool, the agent can generate input parameters as required. Access to all the tools helps the agent identify the best tool choice based on the conversation stage.

TOOLS:
------
Penny has access to the following tools:
{tools}

We include some guidelines that the agent needs to follow while generating outputs. By using emotion-based prompt engineering, we minimize hallucinations and deviation from expected outputs. These guidelines were chosen after extensive testing to minimize edge cases and help prevent common agent mistakes.

<GUIDELINES>

1. If you ever assume any user response without asking, it may cause significant consequences.
2. It is of high priority that you respond and use appropriate tools in their respective stages. If not, it may cause significant consequences.
3. It is of high priority that you never reveal the tools or tool names to the user. Only communicate the outcome.
4. It is critical that you never reveal any details provided by the System including file names. 
5. If ever the user deviates by asking general question during your account opening process, Retrieve the necessary information using 'ProductSearch' tool and answer the question. With confidence, ask user if they want to resume the account opening process and continue from where we left off. 

The agent uses the ReAct framework to make decisions about how to respond based on user input. ReAct provides the agent with a thinking structure, through which it selects the most appropriate tool for a given task. Such frameworks make LLM agents versatile and adaptable to different use cases.

Based on the stage descriptions and the tools available, if the LLM generates a response that requires access to an external tool, then the response of the LLM will include Thought, Decision, Action, Action Input and Observation. The agent comes with a string matcher, which will detect Action and Action Input from the LLM’s response and trigger the respective tool. Based on the response from the tool, the LLM with decide whether to proceed with the Final Answer, and then the output will be returned by the agent.

FORMAT:
------

To use a tool, please always use the following format:
```
Thought: {input}
Decision: Do I need to use a tool? y
Action: what tool to use, should be one of [{tool_names}]
Action Input: the input to the action
Observation: the result of the action
```
When I am finished, I will have a response like this: 

Final Answer: [your response as a banking assistant]

Finally, we give the agent access to the conversation history to better decide what stage the conversation is currently in. In addition, we also give access to an agent scratchpad where it can store its thought processes to execute certain actions.

Be confident that you are a banking assistant and only respond with final answer.
Begin!

<Conversation history>
{conversation_history}

{agent_scratchpad}

Orchestrating intelligent digital assistants requires thoughtful prompt engineering to handle complex tasks. By structuring the conversation into stages, providing tooling, and setting guidelines, we enable the assistant to systematically complete the onboarding process. This approach allows assistants to scale across use cases while maintaining accuracy. With the right guardrails, assistants can deliver smooth, trustworthy customer experiences.

Prompt design is key to unlocking the versatility of LLMs for real-world automation. Amazon Bedrock Prompt Management can be used to streamline the creation, evaluation, versioning and testing of prompts. This will help developers and prompt engineers save time by applying the same prompt to different onboarding processes. When you create a prompt, you can select a different model for inference and adjust the variables to obtain the best-suited results for a variety of workflows.

The following sections explain how to deploy the solution in your AWS account.

Note: Running this workload would have an estimated hourly cost of $1.34 for the Oregon (us-west-2) AWS Region. Check the pricing details for each service to understand the costs you might be charged for different usage tiers and resource configurations.

Setup

To deploy the agent, visit the project Github Repository, and use the following instructions:

1. Ensure the pre-requisites are completed as mentioned in the README.
2. Deploy the solution including the agent, tools infrastructure, and demo application—in that order—based on the instructions in the README.
3. After the deployment is successful, visit the outputted domain where the demo application is running. You can now begin testing the agent.

Testing the agent

Begin your exploration by accessing the Amplify endpoint where the demonstration is hosted. The demonstration incorporates an interactive chat interface, enabling you to engage in a conversational exchange with the digital assistant, Penny. Whenever you want to initiate a new instance of the agent, refresh the web page.

Let’s start talking to Penny:

1. Enter Hi

Penny will respond with a friendly greeting

2. Enter What are the cutoff times to receive wire transfers on the same day?

Penny will use the ProductSearch tool to find the relevant information from the loaded product catalog. You can try asking other questions about the bank’s product or services including the AnyBank Travel Rewards Visa Infinite Card or New Vehicle Loans.

3. Enter I would like to open a new bank account

Penny will recognize that the account opening flow needs to be initiated and will proceed with the first step, which is asking you for an email address.

4. Enter the verified customer email you registered with the Amazon SES identity. For our demonstration, we will use anup@test.com(parameter SesCustomerEmail used in the example command to setup infrastructure)

Penny will take the email address and run the EmailValidation Tool. If there is an existing account with this email, it will ask you to retry. Otherwise, it will move on to the next step which is gathering your account type.

5. Enter I want a savings account or indicate that you want a checking account.

Penny will record your account type and move on to the KYC questions.

6. Enter Anup

Penny will record your first name and continue gathering the remaining KYC information.

7. Enter Ravi

It will record your last name and prompt you for an ID next. We used Ravi to match the ID document provided below.

8. Download the picture ID. It’s also located at ./api/lambdas/test/passport.png

Upload it to the chat by selecting Choose File.

After uploading the image, you will receive a confirmation message on the chat stating We have received your document. Penny will use ID Verification to compare the name entered during the session to the document. After verification is complete, Penny will prompt you to upload a selfie.

9. Upload the selfie located at ./api/lambdas/test/selfie.png to the chat by selecting Choose File.

After the upload is complete, you will receive a confirmation message on the chat stating We have received your document. Penny will use Selfie Verification to compare the face on the ID to the selfie for a face match. After verification is complete, Penny will prompt you to confirm that you want to proceed.

10. Enter Yes I confirm

Penny will use Create Account to complete the onboarding process and send an email confirmation. It will inform to you of this update in the chat.

Check the customer email you used. The email address specified as the SesCustomerEmail parameter (in this example: anup@test.com) during setup will receive a new email from the email address you set as the SesBankEmail parameter (in this example: owner@anybank.com).

11. Go to the DynamoDB console, select Table from the navigation pane and select the table created by the AWS CloudFormation This is the accounts table in the bank’s AWS account. From the Table page, choose Explore items. You will see a new account created with the details that you entered.

Guardrails and security

Security is a critical part of any application and must be rigorously addressed when developing and deploying solutions, especially those that involve handling sensitive data or interacting with users. For a solution similar to the example in this post, several robust security measures should be implemented to maintain the confidentiality, integrity, and availability of the system.

• Address the security of the service itself. One approach to mitigate potential biases, toxicity, or other undesirable outputs is to use Constitutional AI techniques, such as those provided by the LangChain library or Guardrails for Amazon Bedrock. By defining and enforcing a set of rules or constraints, the system can be trained to generate outputs that align with predefined ethical principles and values, thereby enhancing the trustworthiness and reliability of the service.
• To maintain data protection and privacy, implementing a write-only database architecture is recommended. In this setup, the agent or service can write data to the database but is prohibited from reading or retrieving sensitive stored information. This measure effectively isolates sensitive user data, making sure that the agent would be unable to access or disclose confidential details even in the event of a compromise.
• Prompt injection attacks, where malicious inputs are crafted to manipulate the system’s behavior, are a serious concern in conversational AI systems. To mitigate this risk, it’s crucial to implement robust input validation and sanitization mechanisms. This could include techniques like whitelisting permissible characters, filtering out potentially harmful patterns, and employing context-aware input processing.
• Secure coding practices, such as input validation, output encoding, and proper error handling, should be rigorously followed throughout the development process. Regular security audits, penetration testing, and vulnerability assessments should be conducted to identify and address potential weaknesses in the system.
• Amazon API Gateway, a fully managed service, securely handles API traffic, acting as a front door for applications running on AWS. It supports multiple security mechanisms, including AWS Identity and Access Management (IAM) for authentication and authorization, AWS WAF for web application protection, AWS Secrets Manager for securely storing and retrieving secrets, and integration with AWS CloudTrail for API activity logging. API Gateway also supports client-side SSL certificates, API keys, and resource policies for granular access control.
• Communication between users, the solution, and its internal dependencies should be protected using TLS to encrypt data in transit.
• Additionally, the data should be encrypted using data-at-rest encryption with AWS Key Management Service (AWS KMS) customer managed keys (CMK).

By implementing these robust security measures and fostering a culture of continuous security awareness and improvement, the solution can better protect against potential threats, safeguard user privacy, and maintain the integrity and reliability of the service.

Cleanup

Follow the cleanup Instructions in the README of the Github repository to remove the environment from your account.

Conclusion

In this post, we presented an end-to-end solution that demonstrates how banks can transform user onboarding with an AI-powered digital assistant. By orchestrating workflows across AWS services, we enabled automated, secure account opening within minutes. The conversational interface delivers exceptional customer experiences while reducing operational costs.

This solution can be quickly deployed and enhanced using the features of Amazon Bedrock. Amazon Bedrock Agents streamlines workflows by executing multistep tasks and integrating with company systems and data sources. Amazon Bedrock Knowledge Bases provides contextual information from proprietary data sources, enhancing the accuracy and relevance of responses. Additionally, Amazon Bedrock Guardrails implements safeguards to enable responsible AI usage, filtering harmful content and protecting sensitive information. These can enable a robust and secure deployment of an AI-powered onboarding solution.

Key outcomes of this solution include:

• Fully digital onboarding without paper forms or branch visits
• Automated KYC verification using documents and facial recognition
• Customers onboarded securely in minutes with email confirmations
• Lower costs by reducing manual verification workloads
• Personalized assistance for any product questions 24/7

Instant, secure, and scalable delivery has become the norm that customers demand. This AI assistant solution, powered by AWS, showcases the potential future of user onboarding for financial institutions. As consumer behaviors and expectations continue to be influenced by the latest digital experiences across industries, banks that invest in advanced technologies will gain a competitive edge over their rivals.

Ready to future proof your banking experience? Visit Artificial Intelligence and Machine learning for Financial services with AWS.

About the authors

Anup Ravindranath is a Senior Solutions Architect at Amazon Web Services (AWS) based in Toronto, Canada working with Financial Services organizations. He helps customers to transform their businesses and innovate on cloud.

Arya Subramanyam is a Solutions Architect based in Toronto, Canada. She works with Enterprise Greenfield customers as well as Small & Medium businesses as a technical advisor, helping them solve business challenges with cloud solutions. Arya holds a Bachelor of Applied Science in Computer Engineering from the University of British Columbia, Vancouver. Her passion for Generative AI has led her to develop various solutions leveraging Large Language Models (LLMs) with a focus on prompt engineering and AI agents.

Venkata Satyanarayana Chivatam is a Solutions Architect at AWS. He specializes in Generative AI and Computer Vision, with a particular focus on driving adoption across industries such as healthcare and finance. At AWS, he helps ISV and SMB customers leverage cutting-edge AI technologies to unlock new possibilities and solve complex challenges. He is passionate about supporting businesses of all sizes in their AI journey.

Akshata Ramesh Rao is a Solutions Architect in Toronto, Canada. Akshata works with enterprise customers to accelerate innovation and advise them through technical challenges. She also loves working with SMB customers and help them reach their business objectives quickly, safely, and cost-effectively with AWS services, frameworks, and best practices. Prior to joining AWS, Akshata worked as Devops Engineer at Amazon and holds a master’s degree in computer science from University of Ottawa.

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Despite the proliferation of information and data in business environments, employees and stakeholders often find themselves searching for information and struggling to get their questions answered quickly and efficiently. This can lead to productivity losses, frustration, and delays in decision-making.

A generative AI Slack chat assistant can help address these challenges by providing a readily available, intelligent interface for users to interact with and obtain the information they need. By using the natural language processing and generation capabilities of generative AI, the chat assistant can understand user queries, retrieve relevant information from various data sources, and provide tailored, contextual responses.

By harnessing the power of generative AI and Amazon Web Services (AWS) services Amazon Bedrock, Amazon Kendra, and Amazon Lex, this solution provides a sample architecture to build an intelligent Slack chat assistant that can streamline information access, enhance user experiences, and drive productivity and efficiency within organizations.

Why use Amazon Kendra for building a RAG application?

Amazon Kendra is a fully managed service that provides out-of-the-box semantic search capabilities for state-of-the-art ranking of documents and passages. You can use Amazon Kendra to quickly build high-accuracy generative AI applications on enterprise data and source the most relevant content and documents to maximize the quality of your Retrieval Augmented Generation (RAG) payload, yielding better large language model (LLM) responses than using conventional or keyword-based search solutions. Amazon Kendra offers simple-to-use deep learning search models that are pre-trained on 14 domains and don’t require machine learning (ML) expertise. Amazon Kendra can index content from a wide range of sources, including databases, content management systems, file shares, and web pages.

Further, the FAQ feature in Amazon Kendra complements the broader retrieval capabilities of the service, allowing the RAG system to seamlessly switch between providing prewritten FAQ responses and dynamically generating responses by querying the larger knowledge base. This makes it well-suited for powering the retrieval component of a RAG system, allowing the model to access a broad knowledge base when generating responses. By integrating the FAQ capabilities of Amazon Kendra into a RAG system, the model can use a curated set of high-quality, authoritative answers for commonly asked questions. This can improve the overall response quality and user experience, while also reducing the burden on the language model to generate these basic responses from scratch.

This solution balances retaining customizations in terms of model selection, prompt engineering, and adding FAQs with not having to deal with word embeddings, document chunking, and other lower-level complexities typically required for RAG implementations.

Solution overview

The chat assistant is designed to assist users by answering their questions and providing information on a variety of topics. The purpose of the chat assistant is to be an internal-facing Slack tool that can help employees and stakeholders find the information they need.

The architecture uses Amazon Lex for intent recognition, AWS Lambda for processing queries, Amazon Kendra for searching through FAQs and web content, and Amazon Bedrock for generating contextual responses powered by LLMs. By combining these services, the chat assistant can understand natural language queries, retrieve relevant information from multiple data sources, and provide humanlike responses tailored to the user’s needs. The solution showcases the power of generative AI in creating intelligent virtual assistants that can streamline workflows and enhance user experiences based on model choices, FAQs, and modifying system prompts and inference parameters.

Architecture diagram

The following diagram illustrates a RAG approach where the user sends a query through the Slack application and receives a generated response based on the data indexed in Amazon Kendra. In this post, we use Amazon Kendra Web Crawler as the data source and include FAQs stored on Amazon Simple Storage Service (Amazon S3). See Data source connectors for a list of supported data source connectors for Amazon Kendra.

The step-by-step workflow for the architecture is the following:

1. The user sends a query such as What is the AWS Well-Architected Framework? through the Slack app.
2. The query goes to Amazon Lex, which identifies the intent.
3. Currently two intents are configured in Amazon Lex (Welcome and FallbackIntent).
4. The welcome intent is configured to respond with a greeting when a user enters a greeting such as “hi” or “hello.” The assistant responds with “Hello! I can help you with queries based on the documents provided. Ask me a question.”
5. The fallback intent is fulfilled with a Lambda function.
   1. The Lambda function searches Amazon Kendra FAQs through the search_Kendra_FAQ method by taking the user query and Amazon Kendra index ID as inputs. If there’s a match with a high confidence score, the answer from the FAQ is returned to the user.

      def search_Kendra_FAQ(question, kendra_index_id):
          """
          This function takes in the question from the user, and checks if the question exists in the Kendra FAQs.
          :param question: The question the user is asking that was asked via the frontend input text box.
          :param kendra_index_id: The kendra index containing the documents and FAQs
          :return: If found in FAQs, returns the answer along with any relevant links. If not, returns False and then calls kendra_retrieve_document function.
          """
          kendra_client = boto3.client('kendra')
          response = kendra_client.query(IndexId=kendra_index_id, QueryText=question, QueryResultTypeFilter='QUESTION_ANSWER')
          for item in response['ResultItems']:
              score_confidence = item['ScoreAttributes']['ScoreConfidence']
              # Taking answers from FAQs that have a very high confidence score only
              if score_confidence == 'VERY_HIGH' and len(item['AdditionalAttributes']) > 1:
                  text = item['AdditionalAttributes'][1]['Value']['TextWithHighlightsValue']['Text']
                  url = "None"
                  if item['DocumentURI'] != '':
                      url = item['DocumentURI']
                  return (text, url)
          return (False, False)

   2. If there isn’t a match with a high enough confidence score, relevant documents from Amazon Kendra with a high confidence score are retrieved through the kendra_retrieve_document method and sent to Amazon Bedrock to generate a response as the context.

      def kendra_retrieve_document(question, kendra_index_id):
          """
          This function takes in the question from the user, and retrieves relevant passages based on default PageSize of 10.
          :param question: The question the user is asking that was asked via the frontend input text box.
          :param kendra_index_id: The kendra index containing the documents and FAQs
          :return: Returns the context to be sent to the LLM and document URIs to be returned as relevant data sources.
          """
          kendra_client = boto3.client('kendra')
          documents = kendra_client.retrieve(IndexId=kendra_index_id, QueryText=question)
          text = ""
          uris = set()
          if len(documents['ResultItems']) > 0:
              for i in range(len(documents['ResultItems'])):
                  score_confidence = documents['ResultItems'][i]['ScoreAttributes']['ScoreConfidence']
                  if score_confidence == 'VERY_HIGH' or score_confidence == 'HIGH':
                      text += documents['ResultItems'][i]['Content'] + "n"
                      uris.add(documents['ResultItems'][i]['DocumentURI'])
          return (text, uris)

   3. The response is generated from Amazon Bedrock with the invokeLLM method. The following is a snippet of the invokeLLM method within the fulfillment function. Read more on inference parameters and system prompts to modify parameters that are passed into the Amazon Bedrock invoke model request.

      def invokeLLM(question, context, modelId):
          """
          This function takes in the question from the user, along with the Kendra responses as context to generate an answer
          for the user on the frontend.
          :param question: The question the user is asking that was asked via the frontend input text box.
          :param documents: The response from the Kendra document retrieve query, used as context to generate a better
          answer.
          :return: Returns the final answer that will be provided to the end-user of the application who asked the original
          question.
          """
          # Setup Bedrock client
          bedrock = boto3.client('bedrock-runtime')
          # configure model specifics such as specific model
          modelId = modelId
      
          # body of data with parameters that is passed into the bedrock invoke model request
          body = json.dumps({"max_tokens": 350,
                  "system": "You are a truthful AI assistant. Your goal is to provide informative and substantive responses to queries based on the documents provided. If you do not know the answer to a question, you truthfully say you do not know.",
                  "messages": [{"role": "user", "content": "Answer this user query:" + question + "with the following context:" + context}],
                  "anthropic_version": "bedrock-2023-05-31",
                      "temperature":0,
                  "top_k":250,
                  "top_p":0.999})
      
          # Invoking the bedrock model with your specifications
          response = bedrock.invoke_model(body=body,
                                          modelId=modelId)
          # the body of the response that was generated
          response_body = json.loads(response.get('body').read())
          # retrieving the specific completion field, where you answer will be
          answer = response_body.get('content')
          # returning the answer as a final result, which ultimately gets returned to the end user
          return answer

   4. Finally, the response generated from Amazon Bedrock along with the relevant referenced URLs are returned to the end user.

   When selecting websites to index, adhere to the AWS Acceptable Use Policy and other AWS terms. Remember that you can only use Amazon Kendra Web Crawler to index your own web pages or web pages that you have authorization to index. Visit the Amazon Kendra Web Crawler data source guide to learn more about using the web crawler as a data source. Using Amazon Kendra Web Crawler to aggressively crawl websites or web pages you don’t own is not considered acceptable use.

   Supported features

   The chat assistant supports the following features:

   1. Support for the following Anthropic’s models on Amazon Bedrock:
      • claude-v2
      • claude-3-haiku-20240307-v1:0
      • claude-instant-v1
      • claude-3-sonnet-20240229-v1:0
   2. Support for FAQs and the Amazon Kendra Web Crawler data source
   3. Returns FAQ answers only if the confidence score is VERY_HIGH
   4. Retrieves only documents from Amazon Kendra that have a HIGH or VERY_HIGH confidence score
   5. If documents with a high confidence score aren’t found, the chat assistant returns “No relevant documents found”

   Prerequisites

   To perform the solution, you need to have following prerequisites:

   • Basic knowledge of AWS
   • An AWS account with access to Amazon S3 and Amazon Kendra
   • An S3 bucket to store your documents. For more information, see Step 1: Create your first S3 bucket and the Amazon S3 User Guide.
   • A Slack workspace to integrate the chat assistant
   • Permission to install Slack apps in your Slack workspace
   • Seed URLs for the Amazon Kendra Web Crawler data source
     • You’ll need authorization to crawl and index any websites provided
   • AWS CloudFormation for deploying the solution resources

   Build a generative AI Slack chat assistant

   To build a Slack application, use the following steps:

   1. Request model access on Amazon Bedrock for all Anthropic models
   2. Create an S3 bucket in the us-east-1 (N. Virginia) AWS Region.
   3. Upload the AIBot-LexJson.zip and SampleFAQ.csv files to the S3 bucket
   4. Launch the CloudFormation stack in the us-east-1 (N. Virginia) AWS Region.
   5. Enter a Stack name of your choice
   6. For S3BucketName, enter the name of the S3 bucket created in Step 2
   7. For S3KendraFAQKey, enter the name of the SampleFAQs uploaded to the S3 bucket in Step 3
   8. For S3LexBotKey, enter the name of the Amazon Lex .zip file uploaded to the S3 bucket in Step 3
   9. For SeedUrls, enter up to 10 URLs for the web crawler as a comma delimited list. In the example in this post, we give the publicly available Amazon Bedrock service page as the seed URL
   10. Leave the rest as defaults. Choose Next. Choose Next again on the Configure stack options
   11. Acknowledge by selecting the box and choose Submit, as shown in the following screenshot
       
   12. Wait for the stack creation to complete
   13. Verify all resources are created
   14. Test on the AWS Management Console for Amazon Lex
       1. On the Amazon Lex console, choose your chat assistant ${YourStackName}-AIBot
       2. Choose Intents
       3. Choose Version 1 and choose Test, as shown in the following screenshot
          
       4. Select the AIBotProdAlias and choose Confirm, as shown in the following screenshot. If you want to make changes to the chat assistant, you can use the draft version, publish a new version, and assign the new version to the AIBotProdAlias. Learn more about Versioning and Aliases.
          
       5. Test the chat assistant with questions such as, “Which AWS service has 11 nines of durability?” and “What is the AWS Well-Architected Framework?” and verify the responses. The following table shows that there are three FAQs in the sample .csv file.
          

          _question	_answer	_source_uri
          Which AWS service has 11 nines of durability?	Amazon S3	https://aws.amazon.com/s3/
          What is the AWS Well-Architected Framework?	The AWS Well-Architected Framework enables customers and partners to review their architectures using a consistent approach and provides guidance to improve designs over time.	https://aws.amazon.com/architecture/well-architected/
          In what Regions is Amazon Kendra available?	Amazon Kendra is currently available in the following AWS Regions: Northern Virginia, Oregon, and Ireland	https://aws.amazon.com/about-aws/global-infrastructure/regional-product-services/

       6. The following screenshot shows the question “Which AWS service has 11 nines of durability?” and its response. You can observe that the response is the same as in the FAQ file and includes a link.
          
       7. Based on the pages you have crawled, ask a question in the chat. For this example, the publicly available Amazon Bedrock page was crawled and indexed. The following screenshot shows the question, “What are agents in Amazon Bedrock?” and and a generated response that includes relevant links.
          
   15. For integration of the Amazon Lex chat assistant with Slack, see Integrating an Amazon Lex V2 bot with Slack. Choose the AIBotProdAlias under Alias in the Channel Integrations

   Run sample queries to test the solution

   1. In Slack, go to the Apps section. In the dropdown menu, choose Manage and select Browse apps.
      
   2. Search for ${AIBot} in App Directory and choose the chat assistant. This will add the chat assistant to the Apps section in Slack. You can now start asking questions in the chat. The following screenshot shows the question “Which AWS service has 11 nines of durability?” and its response. You can observe that the response is the same as in the FAQ file and includes a link.
      
   3. The following screenshot shows the question, “What is the AWS Well-Architected Framework?” and its response.
      
   4. Based on the pages you have crawled, ask a question in the chat. For this example, the publicly available Amazon Bedrock page was crawled and indexed. The following screenshot shows the question, “What are agents in Amazon Bedrock?” and and a generated response that includes relevant links.
      
   5. The following screenshot shows the question, “What is amazon polly?” Because there is no Amazon Polly documentation indexed, the chat assistant responds with “No relevant documents found,” as expected.
      

   These examples show how the chat assistant retrieves documents from Amazon Kendra and provides answers based on the documents retrieved. If no relevant documents are found, the chat assistant responds with “No relevant documents found.”

   Clean up

   To clean up the resources created by this solution:

   1. Delete the CloudFormation stack by navigating to the CloudFormation console
   2. Select the stack you created for this solution and choose Delete
   3. Confirm the deletion by entering the stack name in the provided field. This will remove all the resources created by the CloudFormation template, including the Amazon Kendra index, Amazon Lex chat assistant, Lambda function, and other related resources.

   Conclusion

   This post describes the development of a generative AI Slack application powered by Amazon Bedrock and Amazon Kendra. This is designed to be an internal-facing Slack chat assistant that helps answer questions related to the indexed content. The solution architecture includes Amazon Lex for intent identification, a Lambda function for fulfilling the fallback intent, Amazon Kendra for FAQ searches and indexing crawled web pages, and Amazon Bedrock for generating responses. The post walks through the deployment of the solution using a CloudFormation template, provides instructions for running sample queries, and discusses the steps for cleaning up the resources. Overall, this post demonstrates how to use various AWS services to build a powerful generative AI–powered chat assistant application.

   This solution demonstrates the power of generative AI in building intelligent chat assistants and search assistants. Explore the generative AI Slack chat assistant: Invite your teams to a Slack workspace and start getting answers to your indexed content and FAQs. Experiment with different use cases and see how you can harness the capabilities of services like Amazon Bedrock and Amazon Kendra to enhance your business operations. For more information about using Amazon Bedrock with Slack, refer to Deploy a Slack gateway for Amazon Bedrock.

   ---

   About the authors

   Kruthi Jayasimha Rao is a Partner Solutions Architect with a focus on AI and ML. She provides technical guidance to AWS Partners in following best practices to build secure, resilient, and highly available solutions in the AWS Cloud.

   Mohamed Mohamud is a Partner Solutions Architect with a focus on Data Analytics. He specializes in streaming analytics, helping partners build real-time data pipelines and analytics solutions on AWS. With expertise in services like Amazon Kinesis, Amazon MSK, and Amazon EMR, Mohamed enables data-driven decision-making through streaming analytics.

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In the generative AI era, agents that simulate human actions and behaviors are emerging as a powerful tool for enterprises to create production-ready applications. Agents can interact with users, perform tasks, and exhibit decision-making abilities, mimicking humanlike intelligence. By combining agents with foundation models (FMs) from the Amazon Titan in Amazon Bedrock family, customers can develop multimodal, complex applications that enable the agent to understand and generate natural language or images.

For example, in the fashion retail industry, an assistant powered by agents and multimodal models can provide customers with a personalized and immersive experience. The assistant can engage in natural language conversations, understanding the customer’s preferences and intents. It can then use the multimodal capabilities to analyze images of clothing items and make recommendations based on the customer’s input. Additionally, the agent can generate visual aids, such as outfit suggestions, enhancing the overall customer experience.

In this post, we implement a fashion assistant agent using Amazon Bedrock Agents and the Amazon Titan family models. The fashion assistant provides a personalized, multimodal conversational experience. Among others, the capabilities of Amazon Titan Image Generator to inpaint and outpaint images can be used to generate fashion inspirations and edit user photos. Amazon Titan Multimodal Embeddings models can be used to search for a style on a database using both a prompt text or a reference image provided by the user to find similar styles. Anthropic Claude 3 Sonnet is used by the agent to orchestrate the agent’s actions, for example, search for the current weather to receive weather-appropriate outfit recommendations. A simple web UI through Streamlit provides the user with the best experience to interact with the agent.

The fashion assistant agent can be smoothly integrated into existing ecommerce platforms or mobile applications, providing customers with a seamless and delightful experience. Customers can upload their own images, describe their desired style, or even provide a reference image, and the agent will generate personalized recommendations and visual inspirations.

The code used in this solution is available in the GitHub repository.

Solution overview

The fashion assistant agent uses the power of Amazon Titan models and Amazon Bedrock Agents to provide users with a comprehensive set of style-related functionalities:

• Image-to-image or text-to-image search – This tool allows customers to find products similar to styles they like from the catalog, enhancing their user experience. We use the Titan Multimodal Embeddings model to embed each product image and store them in Amazon OpenSearch Serverless for future retrieval.
• Text-to-image generation – If the desired style is not available in the database, this tool generates unique, customized images based on the user’s query, enabling the creation of personalized styles.
• Weather API connection – By fetching weather information for a given location mentioned in the user’s prompt, the agent can suggest appropriate styles for the occasion, making sure the customer is dressed for the weather.
• Outpainting – Users can upload an image and request to change the background, allowing them to visualize their preferred styles in different settings.
• Inpainting – This tool enables users to modify specific clothing items in an uploaded image, such as changing the design or color, while keeping the background intact.

The following flow chart illustrates the decision-making process:

And the corresponding architecture diagram:

Prerequisites

To set up the fashion assistant agent, make sure you have the following:

• An active AWS account and AWS Identity and Access Management (IAM) role with Amazon Bedrock, AWS Lambda, and Amazon Simple Storage (Amazon S3) access
• Installation of required Python libraries such as Streamlit
• Anthropic Claude 3 Sonnet, Amazon Titan Image Generator and Amazon Titan Multimodal Embeddings models enabled in Amazon Bedrock. You can confirm these are enabled on the Model access page of the Amazon Bedrock console. If these models are enabled, the access status will show as Access granted, as shown in the following screenshot.

Before executing the notebook provided in the GitHub repo to start building the infrastructure, make sure your AWS account has permission to:

• Create managed IAM roles and policies
• Create and invoke Lambda functions
• Create, read from, and write to S3 buckets
• Access and manage Amazon Bedrock agents and models

If you want to enable the image-to-image or text-to-image search capabilities, additional permissions for your AWS account are required:

• Create security policy, access policy, collect, index, and index mapping on OpenSearch Serverless
• Call the BatchGetCollection on OpenSearch Serverless

Set up the fashion assistant agent

To set up the fashion assistant agent, follow these steps:

1. Clone the GitHub repository using the command

   git clone

2. Complete the prerequisites to grant sufficient permissions
3. Follow the deployment steps outlined in the README.md
4. (Optional) If you want to use the image_lookup feature, execute code snippets in opensearch_ingest.ipynb to use Amazon Titan Multimodal Embeddings to embed and store sample images
5. Run the Streamlit UI to interact with the agent using the command

   streamlit run frontend/app.py

By following these steps, you can create a powerful and engaging fashion assistant agent that combines the capabilities of Amazon Titan models with the automation and decision-making capabilities of Amazon Bedrock Agents.

Test the fashion assistant

After the fashion assistant is set up, you can interact with it through the Streamlit UI. Follow these steps:

1. Navigate to your Streamlit UI, as shown in the following screenshot

2. Upload an image or enter a text prompt describing the desired style, according to the desired action, for example, image search, image generation, outpainting, or inpainting. The following screenshot shows an example prompt.

3. Press enter to send the prompt to the agent. You can view the chain-of-thought (CoT) process of the agent in the UI, as shown in the following screenshot

4. When the response is ready, you can view the agent’s response in the UI, as shown in the following screenshot. The response may include generated images, similar style recommendations, or modified images based on your request. You can download the generated images directly from the UI or check the image in your S3 bucket.

Clean up

To avoid unnecessary costs, make sure to delete the resources used in this solution. You can do this by running the following command.

cdk destroy

Conclusion

The fashion assistant agent, powered by Amazon Titan models and Amazon Bedrock Agents, is an example of how retailers can create innovative applications that enhance the customer experience and drive business growth. By using this solution, retailers can gain a competitive edge, offering personalized style recommendations, visual inspirations, and interactive fashion advice to their customers.

We encourage you to explore the potential of building more agents like this fashion assistant by checking out the examples available on the aws-samples GitHub repository.

 About the Authors

Akarsha Sehwag is a Data Scientist and ML Engineer in AWS Professional Services with over 5 years of experience building ML based solutions. Leveraging her expertise in Computer Vision and Deep Learning, she empowers customers to harness the power of the ML in AWS cloud efficiently. With the advent of Generative AI, she worked with numerous customers to identify good use-cases, and building it into production-ready solutions.

Yanyan Zhang is a Senior Generative AI Data Scientist at Amazon Web Services, where she has been working on cutting-edge AI/ML technologies as a Generative AI Specialist, helping customers leverage GenAI to achieve their desired outcomes. Yanyan graduated from Texas A&M University with a Ph.D. degree in Electrical Engineering. Outside of work, she loves traveling, working out and exploring new things.

Antonia Wiebeler is a Data Scientist at the AWS Generative AI Innovation Center, where she enjoys building proofs of concept for customers. Her passion is exploring how generative AI can solve real-world problems and create value for customers. While she is not coding, she enjoys running and competing in triathlons.

Alex Newton is a Data Scientist at the AWS Generative AI Innovation Center, helping customers solve complex problems with generative AI and machine learning. He enjoys applying state of the art ML solutions to solve real world challenges. In his free time you’ll find Alex playing in a band or watching live music.

Chris Pecora is a Generative AI Data Scientist at Amazon Web Services. He is passionate about building innovative products and solutions while also focused on customer-obsessed science. When not running experiments and keeping up with the latest developments in generative AI, he loves spending time with his kids.

Maira Ladeira Tanke is a Senior Generative AI Data Scientist at AWS. With a background in machine learning, she has over 10 years of experience architecting and building AI applications with customers across industries. As a technical lead, she helps customers accelerate their achievement of business value through generative AI solutions on Amazon Bedrock. In her free time, Maira enjoys traveling, playing with her cat, and spending time with her family someplace warm.

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This post is co-written with Dean Steel and Simon Gatie from Aviva.

With a presence in 16 countries and serving over 33 million customers, Aviva is a leading insurance company headquartered in London, UK. With a history dating back to 1696, Aviva is one of the oldest and most established financial services organizations in the world. Aviva’s mission is to help people protect what matters most to them—be it their health, home, family, or financial future. To achieve this effectively, Aviva harnesses the power of machine learning (ML) across more than 70 use cases. Previously, ML models at Aviva were developed using a graphical UI-driven tool and deployed manually. This approach led to data scientists spending more than 50% of their time on operational tasks, leaving little room for innovation, and posed challenges in monitoring model performance in production.

In this post, we describe how Aviva built a fully serverless MLOps platform based on the AWS Enterprise MLOps Framework and Amazon SageMaker to integrate DevOps best practices into the ML lifecycle. This solution establishes MLOps practices to standardize model development, streamline ML model deployment, and provide consistent monitoring. We illustrate the entire setup of the MLOps platform using a real-world use case that Aviva has adopted as its first ML use case.

The Challenge: Deploying and operating ML models at scale

Approximately 47% of ML projects never reach production, according to Gartner. Despite the advancements in open source data science frameworks and cloud services, deploying and operating these models remains a significant challenge for organizations. This struggle highlights the importance of establishing consistent processes, integrating effective monitoring, and investing in the necessary technical and cultural foundations for a successful MLOps implementation.

For companies like Aviva, which handles approximately 400,000 insurance claims annually, with expenditures of about £3 billion in settlements, the pressure to deliver a seamless digital experience to customers is immense. To meet this demand amidst rising claim volumes, Aviva recognizes the need for increased automation through AI technology. Therefore, developing and deploying more ML models is crucial to support their growing workload.

To prove the platform can handle onboarding and industrialization of ML models, Aviva picked their Remedy use case as their first project. This use case concerns a claim management system that employs a data-driven approach to determine whether submitted car insurance claims qualify as either total loss or repair cases, as illustrated in the following diagram

1. The workflow consists of the following steps:
2. The workflow begins when a customer experiences a car accident.
3. The customer contacts Aviva, providing information about the incident and details about the damage.
4. To determine the estimated cost of repair, 14 ML models and a set of business rules are used to process the request.
5. The estimated cost is compared with the car’s current market value from external data sources.
6. Information related to similar cars for sale nearby is included in the analysis.
7. Based on the processed data, a recommendation is made by the model to either repair or write off the car. This recommendation, along with the supporting data, is provided to the claims handler, and the pipeline reaches its final state.

The successful deployment and evaluation of the Remedy use case on the MLOps platform was intended to serve as a blueprint for future use cases, providing maximum efficiency by using templated solutions.

Solution overview of the MLOps platform

To handle the complexity of operationalizing ML models at scale, AWS offers provides an MLOps offering called AWS Enterprise MLOps Framework, which can be used for a wide variety of use cases. The offering encapsulates a best practices approach to build and manage MLOps platforms based on the consolidated knowledge gained from a multitude of customer engagements carried out by AWS Professional Services in the last five 5 years. The proposed baseline architecture can be logically divided into four building blocks which that are sequentially deployed into the provided AWS accounts, as illustrated in the following diagram below.

The building blocks are as follows:

• Networking – A virtual private cloud (VPC), subnets, security groups, and VPC endpoints are deployed across all accounts.
• Amazon SageMaker Studio – SageMaker Studio offers a fully integrated ML integrated development environment (IDE) acting as a data science workbench and control panel for all ML workloads.
• Amazon SageMaker Projects templates – These ready-made infrastructure sets cover the ML lifecycle, including continuous integration and delivery (CI/CD) pipelines and seed code. You can launch these from SageMaker Studio with a few clicks, either choosing from preexisting templates or creating custom ones.
• Seed code – This refers to the data science code tailored for a specific use case, divided between two repositories: training (covering processing, training, and model registration) and inference (related to SageMaker endpoints). The majority of time in developing a use case should be dedicated to modifying this code.

The framework implements the infrastructure deployment from a primary governance account to separate development, staging, and production accounts. Developers can use the AWS Cloud Development Kit (AWS CDK) to customize the solution to align with the company’s specific account setup. In adapting the AWS Enterprise MLOps Framework to a three-account structure, Aviva has designated accounts as follows: development, staging, and production. This structure is depicted in the following architecture diagram. The governance components, which facilitate model promotions with consistent processes across accounts, have been integrated into the development account.

Building reusable ML pipelines

The processing, training, and inference code for the Remedy use case was developed by Aviva’s data science team in SageMaker Studio, a cloud-based environment designed for collaborative work and rapid experimentation. When experimentation is complete, the resulting seed code is pushed to an AWS CodeCommit repository, initiating the CI/CD pipeline for the construction of a SageMaker pipeline. This pipeline comprises a series of interconnected steps for data processing, model training, parameter tuning, model evaluation, and the registration of the generated models in the Amazon SageMaker Model Registry.

Amazon SageMaker Automatic Model Tuning enabled Aviva to utilize advanced tuning strategies and overcome the complexities associated with implementing parallelism and distributed computing. The initial step involved a hyperparameter tuning process (Bayesian optimization), during which approximately 100 model variations were trained (5 steps with 20 models trained concurrently in each step). This feature integrates with Amazon SageMaker Experiments to provide data scientists with insights into the tuning process. The optimal model is then evaluated in terms of accuracy, and if it exceeds a use case-specific threshold, it is registered in the SageMaker Model Registry. A custom approval step was constructed, such that only Aviva’s lead data scientist can permit the deployment of a model through a CI/CD pipeline to a SageMaker real-time inference endpoint in the development environment for further testing and subsequent promotion to the staging and production environment.

Serverless workflow for orchestrating ML model inference

To realize the actual business value of Aviva’s ML model, it was necessary to integrate the inference logic with Aviva’s internal business systems. The inference workflow is responsible for combining the model predictions, external data, and business logic to generate a recommendation for claims handlers. The recommendation is based on three possible outcomes:

• Write off a vehicle (expected repairs cost exceeds the value of the vehicle)
• Seek a repair (value of the vehicle exceeds repair cost)
• Require further investigation given a borderline estimation of the value of damage and the price for a replacement vehicle

The following diagram illustrates the workflow.

The workflow starts with a request to an API endpoint hosted on Amazon API Gateway originating from a claims management system, which invokes an AWS Step Functions workflow that uses AWS Lambda to complete the following steps:

1. The input data of the REST API request is transformed into encoded features, which is utilized by the ML model.
2. ML model predictions are generated by feeding the input to the SageMaker real-time inference endpoints. Because Aviva processes daily claims at irregular intervals, real-time inference endpoints help overcome the challenge of providing predictions consistently at low latency.
3. ML model predictions are further processed by a custom business logic to derive a final decision (of the three aforementioned options).
4. The final decision, along with the generated data, is consolidated and transmitted back to the claims management system as a REST API response.

Monitor ML model decisions to elevate confidence amongst users

The ability to obtain real-time access to detailed data for each state machine run and task is critically important for effective oversight and enhancement of the system. This includes providing claim handlers with comprehensive details behind decision summaries, such as model outputs, external API calls, and applied business logic, to make sure recommendations are based on accurate and complete information. Snowflake is the preferred data platform, and it receives data from Step Functions state machine runs through Amazon CloudWatch logs. A series of filters screen for data pertinent to the business. This data then transmits to an Amazon Data Firehose delivery stream and subsequently relays to an Amazon Simple Storage Service (Amazon S3) bucket, which is accessed by Snowflake. The data generated by all runs is used by Aviva business analysts to create dashboards and management reports, facilitating insights such as monthly views of total losses by region or average repair costs by vehicle manufacturer and model.

Security

The described solution processes personally identifiable information (PII), making customer data protection the core security focus of the solution. The customer data is protected by employing networking restrictions, because processing is run inside the VPC, where data is logically separated in transit. The data is encrypted in transit between steps of the processing and encrypted at rest using AWS Key Management Service (AWS KMS). Access to the production customer data is restricted on a need-to-know basis, where only the authorized parties are allowed to access production environment where this data resides.

The second security focus of the solution is protecting Aviva’s intellectual property. The code the data scientists and engineers are working on is stored securely in the dev AWS account, private to Aviva, in the CodeCommit git repositories. The training data and the artifacts of the trained models are stored securely in the S3 buckets in the dev account, protected by AWS KMS encryption at rest, with AWS Identity and Access Management (IAM) policies restricting access to the buckets to only the authorized SageMaker endpoints. The code pipelines are private to the account as well, and reside in the customer’s AWS environment.

The auditability of the workflows is provided by logging the steps of inference and decision-making in the CloudWatch logs. The logs are encrypted at rest as well with AWS KMS, and are configured with a lifecycle policy, guaranteeing availability of audit information for the required compliance period. To maintain security of the project and operate it securely, the accounts are enabled with Amazon GuardDuty and AWS Config. AWS CloudTrail is used to monitor the activity within the accounts. The software to monitor for security vulnerabilities resides primarily in the Lambda functions implementing the business workflows. The processing code is primarily written in Python using libraries that are periodically updated.

Conclusion

This post provided an overview of the partnership between Aviva and AWS, which resulted in the construction of a scalable MLOps platform. This platform was developed using the open source AWS Enterprise MLOps Framework, which integrated DevOps best practices into the ML lifecycle. Aviva is now capable of replicating consistent processes and deploying hundreds of ML use cases in weeks rather than months. Furthermore, Aviva has transitioned entirely to a pay-as-you-go model, resulting in a 90% reduction in infrastructure costs compared to the company’s previous on-premises ML platform solution.

Explore the AWS Enterprise MLOps Framework on GitHub and learn more about MLOps on Amazon SageMaker to see how it can accelerate your organization’s MLOps journey.

About the Authors

Dean Steel is a Senior MLOps Engineer at Aviva with a background in Data Science and actuarial work. He is passionate about all forms of AI/ML with experience developing and deploying a diverse range of models for insurance-specific applications, from large transformers through to linear models. With an engineering focus, Dean is a strong advocate of combining AI/ML with DevSecOps in the cloud using AWS. In his spare time, Dean enjoys exploring music technology, restaurants and film.

Simon Gatie, Principle Analytics Domain Authority at Aviva in Norwich brings a diverse background in Physics, Accountancy, IT, and Data Science to his role. He leads Machine Learning projects at Aviva, driving innovation in data science and advanced technologies for financial services.

Gabriel Rodriguez is a Machine Learning Engineer at AWS Professional Services in Zurich. In his current role, he has helped customers achieve their business goals on a variety of ML use cases, ranging from setting up MLOps pipelines to developing a fraud detection application. Whenever he is not working, he enjoys doing physical exercises, listening to podcasts, or traveling.

Marco Geiger is a Machine Learning Engineer at AWS Professional Services based in Zurich. He works with customers from various industries to develop machine learning solutions that use the power of data for achieving business goals and innovate on behalf of the customer. Besides work, Marco is a passionate hiker, mountain biker, football player, and hobby barista.

Andrew Odendaal is a Senior DevOps Consultant at AWS Professional Services based in Dubai. He works across a wide range of customers and industries to bridge the gap between software and operations teams and provides guidance and best practices for senior management when he’s not busy automating something. Outside of work, Andrew is a family man that loves nothing more than a binge-watching marathon with some good coffee on tap.

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This post is co-written with Ike Bennion from Visier.

Visier’s mission is rooted in the belief that people are the most valuable asset of every organization and that optimizing their potential requires a nuanced understanding of workforce dynamics.

Paycor is an example of the many world-leading enterprise people analytics companies that trust and use the Visier platform to process large volumes of data to generate informative analytics and actionable predictive insights.

Visier’s predictive analytics has helped organizations such as Providence Healthcare retain critical employees within their workforce and saved an estimated $6 million by identifying and preventing employee attrition by using a framework built on top of Visier’s risk-of-exit predictions.

Trusted sources like Sapient Insights Group, Gartner, G2, Trust Radius, and RedThread Research have recognized Visier for its inventiveness, great user experience, and vendor and customer satisfaction. Today, over 50,000 organizations in 75 countries use the Visier platform as the driver to shape business strategies and drive better business results.

Unlocking growth potential by overcoming the tech stack barrier

Visier’s analytics and predictive power is what makes its people analytics solution so valuable. Users without data science or analytics experience can generate rigorous data-backed predictions to answer big questions like time-to-fill for important positions, or resignation risk for crucial employees.

It was an executive priority at Visier to continue innovating in their analytics and predictive capabilities because those make up one of the cornerstones of what their users love about their product.

The challenge for Visier was that their data science tech stack was holding them back from innovating at the rate they wanted to. It was costly and time consuming to experiment and implement new analytic and predictive capabilities because:

• The data science tech stack was tightly coupled with the entire platform development. The data science team couldn’t roll out changes independently to production. This limited the team to fewer and slower iteration cycles.
• The data science tech stack was a collection of solutions from multiple vendors, which led to additional management and support overhead for the data science team.

Steamlining model management and deployment with SageMaker

Amazon SageMaker is a managed machine learning platform that provides data scientists and data engineers familiar concepts and tools to build, train, deploy, govern, and manage the infrastructure needed to have highly available and scalable model inference endpoints. Amazon SageMaker Inference Recommender is an example of a tool that can help data scientists and data engineers be more autonomous and less reliant on outside teams by providing guidance on right-sizing inference instances.

The existing data science tech stack was one of the many services comprising Visier’s application platform. Using the SageMaker platform, Visier built an API-based microservices architecture for the analytics and predictive services that was decoupled from the application platform. This gave the data science team the desired autonomy to deploy changes independently and release new updates more frequently.

The results

The first improvement Visier saw after migrating the analytics and predictive services to SageMaker was that it allowed the data science team to spend more time on innovations—such as the build-up of a prediction model validation pipeline—rather than having to spend time on deployment details and vendor tooling integration.

Prediction model validation

The following figure shows the prediction model validation pipeline.

Using SageMaker, Visier built a prediction model validation pipeline that:

1. Pulls the training dataset from the production databases
2. Gathers additional validation measures that describe the dataset and specific corrections and enhancements on the dataset
3. Performs multiple cross-validation measurements using different split strategies
4. Stores the validation results along with metadata about the run in a permanent datastore

The validation pipeline allowed the team to deliver a stream of advancements in the models that improved prediction performance by 30% across their whole customer base.

Train customer-specific predictive models at scale

Visier develops and manages thousands of customer-specific predictive models for their enterprise customers. The second workflow improvement the data science team made was to develop a highly scalable method to generate all of the customer-specific predictive models. This allowed the team to deliver ten times as many models with the same number of resources.

 As shown in the preceding figure, the team developed a model-training pipeline where model changes are made in a central prediction codebase. This codebase is executed separately for each Visier customer to train a sequence of custom models (for different points in time) that are sensitive to the specialized configuration of each customer and their data. Visier uses this pattern to scalably push innovation in a single model design to thousands of custom models across their customer base. To ensure state-of-art training efficiency for large models, SageMaker provides libraries that support parallel (SageMaker Model Parallel Library) and distributed (SageMaker Distributed Data Parallelism Library) model training. To learn more about how effective these libraries are, see Distributed training and efficient scaling with the Amazon SageMaker Model Parallel and Data Parallel Libraries.

Using the model validation workload shown earlier, changes made to a predictive model can be validated in as little as three hours.

Process unstructured data

Iterative improvements, a scalable deployment, and consolidation of data science technology were an excellent start, but when Visier adopted SageMaker, the goal was to enable innovation that was entirely out of reach by the previous tech stack.

A unique advantage that Visier has is the ability to learn from the collective employee behaviors across all their customer base. Tedious data engineering tasks like pulling data into the environment and database infrastructure costs were eliminated by securely storing their vast amount of customer-related datasets within Amazon Simple Storage Service (Amazon S3) and using Amazon Athena to directly query the data using SQL. Visier used these AWS services to combine relevant datasets and feed them directly into SageMaker, resulting in the creation and release of a new prediction product called Community Predictions. Visier’s Community Predictions give smaller organizations the power to create predictions based on the entire community’s data, rather than just their own. That gives a 100-person organization access to the kind of predictions that otherwise would be reserved for enterprises with thousands of employees.

For information about how you can manage and process your own unstructured data, see Unstructured data management and governance using AWS AI/ML and analytics services.

Use Visier Data in Amazon SageMaker

With the transformative success Visier had internally, they wanted ensure their end-customers could also benefit from the Amazon SageMaker platform to develop their own AI and machine learning (AI/ML) models.

Visier has written a full tutorial about how to use Visier Data in Amazon SageMaker and have also built a Python connector available on their GitHub repo. The Python connector allows customers to pipe Visier data to their own AI/ML projects to better understand the impact of their people on financials, operations, customers and partners. These results are often then imported back into the Visier platform to distribute these insights and drive derivative analytics to further improve outcomes across the employee lifecycle.

Conclusion

Visier’s success with Amazon SageMaker demonstrates the power and flexibility of this managed machine learning platform. By using the capabilities of SageMaker, Visier increased their model output by 10 times, accelerated innovation cycles, and unlocked new opportunities such as processing unstructured data for their Community Predictions product.

If you’re looking to streamline your machine learning workflows, scale your model deployments, and unlock insights from your data, explore the possibilities with SageMaker and built-in capabilities such as Amazon SageMaker Pipelines.

Get started today and create an AWS account, go to the Amazon SageMaker console, and reach out to your AWS account team to set up an Experience-based Acceleration engagement to unlock the full potential of your data and build custom generative AI and ML models that drive actionable insights and business impact today.

About the authors

Kinman Lam is a Solution Architect at AWS. He is accountable for the health and growth of some of the largest ISV/DNB companies in Western Canada. He is also a member of the AWS Canada Generative AI vTeam and has helped a growing number of Canadian companies successful launch advanced Generative AI use-cases.

Ike Bennion is the Vice President of Platform & Platform Marketing at Visier and a recognized thought leader in the intersection between people, work and technology. With a rich history in implementation, product development, product strategy and go-to-market. He specializes in market intelligence, business strategy, and innovative technologies, including AI and blockchain. Ike is passionate about using data to drive equitable and intelligent decision-making. Outside of work, he enjoys dogs, hip hop, and weightlifting.

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