Monthly Archives: October 2023

Amazon Kendra is an intelligent search service powered by machine learning (ML). Amazon Kendra helps you easily aggregate content from a variety of content repositories into a centralized index that lets you quickly search all your enterprise data and find the most accurate answer. Drupal is a content management software. It’s used to make many of the websites and applications we use every day. Drupal has a great feature set, like straightforward content authoring, reliable performance, and security. Many organizations use Drupal to store their content. One of the key requirements for many customers using Drupal is the ability to easily and securely find accurate information across all the documents in the data source.

With the Amazon Kendra Drupal connector, you can index Drupal content, filter the types of custom content you want to index, and easily search through Drupal content using Amazon Kendra intelligent search.

This post shows you how to use the Amazon Kendra Drupal connector to configure the connector as a data source for your Amazon Kendra index and search your Drupal documents. Based on the configuration of the Drupal connector, you can synchronize the connector to crawl and index different types of Drupal content such as blogs and wikis. The connector also ingests the access control list (ACL) information for each file. The ACL information is used for user context filtering, where search results for a query are filtered by what a user has authorized access to.

Prerequisites

To try out the Amazon Kendra connector for Drupal using this post as a reference, you need the following:

• An AWS account with privileges to create AWS Identity and Access Management (IAM) roles and policies. For more information, see Overview of access management: Permissions and policies and IAM roles for Drupal data sources.
• Basic knowledge of AWS and working knowledge of Drupal administration.
• Drupal set up with a user with the Administrator role. We will store the administrator user name and password in AWS Secrets Manager.

Configure the data source using the Amazon Kendra connector for Drupal

To add a data source to your Amazon Kendra index using the Drupal connector, you can use an existing index or create a new index. Then complete the following steps. For more information on this topic, refer to the Amazon Kendra Developer Guide.

1. On the Amazon Kendra console, open your index and choose Data sources in the navigation pane.
2. Choose Add data source.
3. Under Drupal, choose Add connector.
   
4. In the Specify data source details section, enter a name and description and choose Next.
   
5. On the Define access and security section, for Drupal Host URL, enter the Drupal site URL.
6. To configure the SSL certificates, you can create a self-signed certificate for this setup using the openssl x509 -in mydrupalsite.pem -out drupal.crt command and store the certificate in an Amazon Simple Storage Service (Amazon S3) bucket. For more details on generating a private key and the certificate, refer to Generating Certificates.
7. Choose Browse S3 and choose the S3 bucket with the SSL certificate.
   
8. Under Authentication, you have two options:
   • Use Secrets Manager to create new Drupal authentication credentials. You need a Drupal admin user name and password (additionally, a client ID and client secret for OAuth 2.0 authentication).
   • Use an existing Secrets Manager secret that has the Drupal authentication credentials you want the connector to access (additionally, a client ID and client secret for OAuth 2.0 authentication).
9. Choose Save and add secret.
   
10. For IAM role, choose Create a new role or choose an existing IAM role configured with appropriate IAM policies to access the Secrets Manager secret, Amazon Kendra index, and data source.

Refer to IAM roles for data sources for the required permissions for the IAM role.

11. Choose Next.
    
12. In the Configure sync settings section, select Articles, Basic pages, Basic blocks, Custom content types, and Custom Blocks along with options to crawl comments and attachments as needed.
13. Optionally, enter the include/exclude patterns for the entity titles.
14. Provide information about your sync scope (full or delta only) and specify the run schedule.
15. Choose Next.
    
    
16. In the Set field mappings section, add custom Drupal fields you want to sync and their respective Amazon Kendra field mappings. The required fields are pre-mapped by Amazon Kendra.
    
17. Choose Next.
18. Review the configuration settings and save the data source.
19. Choose Sync now on the created data source to start data synchronization with the Amazon Kendra Index.
    

The time required to crawl and sync the contents into Amazon Kendra varies based on the volume of content and the throughput.

You can now search the indexed Drupal content using the search console or a search application. Optionally, you can search with ACL with the following additional steps.

20. Go to the index page that you created and on the User access control tab, choose Edit settings.
    
21. Under Access control settings, select Yes, keep the default values for Username and Groups, choose JSON for Token type, and keep the user-group expansion as None.
22. On the next page, retain the default values (or change them based on your capacity requirements) and choose Update.
    

Perform intelligent search with Amazon Kendra

Before you try searching on the Amazon Kendra console or using the API, make sure that the data source sync is complete. To check, view the data sources and verify if the last sync was successful.

1. To start your search, on the Amazon Kendra console, choose Search indexed content in the navigation pane.

You’re redirected to the Amazon Kendra search console. Now you can search information from the Drupal documents you indexed using Amazon Kendra.

2. For this post, we search for a document stored in the Drupal data source.
   
3. Expand Test query with an access token and choose Apply token.
4. For Username, enter the email address associated with your Drupal account.
5. Choose Apply.
   

Now the user can only see the content they have access based on the user name or groups specified. In our example, the Drupal user with the test@amazon.com email doesn’t have access to any documents on Drupal, so none are displayed.

Limitations

Note the following limitations when using this solution:

• The content types (such as article, or basic page) that aren’t associated with any view cannot be crawled.
• If an administrator doesn’t have access to a block, then you can’t crawl the data from the block.
• The document body for article, basic page, basic block, user-defined content type, and user-defined block type is displayed in HTML format. If the HTML content is not well-formed, then the HTML related tags will appear in the document body and therefore can be seen on the Amazon Kendra search results. This is the same with comments of article, basic page, basic block, user-defined content type, user-defined block type.
• The content type or block type without description or body will not be injected into the Amazon Kendra index because there is a validation on the Amazon Kendra SDK side. However, Drupal allows you to create the content type without description or body. Only the comments and attachments of the respective content types or block types (if they exist) will be injected into the Amazon Kendra index.

Clean up

To avoid incurring future costs, clean up the resources you created as part of this solution. If you created a new Amazon Kendra index while testing this solution, delete it. If you only added a new data source using the Amazon Kendra connector for Drupal, delete that data source. Delete any IAM users created.

Conclusion

With the Amazon Kendra Drupal connector, your organization can search contents stored in a Drupal site securely using intelligent search powered by Amazon Kendra. In this post, we introduced you to the integration, but there are many additional features that we didn’t cover, such as the following:

• You can map additional fields to Amazon Kendra index attributes and enable them for faceting, search, and display in the search results
• You can integrate the Drupal data source with the Custom Document Enrichment (CDE) capability in Amazon Kendra to perform additional attribute mapping logic and even custom content transformation during ingestion

To learn more about the possibilities with Drupal, refer to the Amazon Kendra Developer Guide.

For more information on other Amazon Kendra built-in connectors for popular data sources, refer to the Amazon Kendra Connectors page.

About the authors

Channa Basavaraja is a Senior Solutions Architect at AWS with over 2 decades of experience building distributed business solutions. His areas of depth span Machine Learning, app/mobile dev, event-driven architecture, and IoT/edge computing.

Yuanhua Wang is a software engineer at AWS with more than 15 years of experience in the technology industry. His interests are software architecture and build tools on cloud computing.

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This is a guest post by Jose Benitez, Founder and Director of AI and Mattias Ponchon, Head of Infrastructure at Intuitivo.

Intuitivo, a pioneer in retail innovation, is revolutionizing shopping with its cloud-based AI and machine learning (AI/ML) transactional processing system. This groundbreaking technology enables us to operate millions of autonomous points of purchase (A-POPs) concurrently, transforming the way customers shop. Our solution outpaces traditional vending machines and alternatives, offering an economical edge with its ten times cheaper cost, easy setup, and maintenance-free operation. Our innovative new A-POPs (or vending machines) deliver enhanced customer experiences at ten times lower cost because of the performance and cost advantages AWS Inferentia delivers. Inferentia has enabled us to run our You Only Look Once (YOLO) computer vision models five times faster than our previous solution and supports seamless, real-time shopping experiences for our customers. Additionally, Inferentia has also helped us reduce costs by 95 percent compared to our previous solution. In this post, we cover our use case, challenges, and a brief overview of our solution using Inferentia.

The changing retail landscape and need for A-POP

The retail landscape is evolving rapidly, and consumers expect the same easy-to-use and frictionless experiences they are used to when shopping digitally. To effectively bridge the gap between the digital and physical world, and to meet the changing needs and expectations of customers, a transformative approach is required. At Intuitivo, we believe that the future of retail lies in creating highly personalized, AI-powered, and computer vision-driven autonomous points of purchase (A-POP). This technological innovation brings products within arm’s reach of customers. Not only does it put customers’ favorite items at their fingertips, but it also offers them a seamless shopping experience, devoid of long lines or complex transaction processing systems. We’re excited to lead this exciting new era in retail.

With our cutting-edge technology, retailers can quickly and efficiently deploy thousands of A-POPs. Scaling has always been a daunting challenge for retailers, mainly due to the logistic and maintenance complexities associated with expanding traditional vending machines or other solutions. However, our camera-based solution, which eliminates the need for weight sensors, RFID, or other high-cost sensors, requires no maintenance and is significantly cheaper. This enables retailers to efficiently establish thousands of A-POPs, providing customers with an unmatched shopping experience while offering retailers a cost-effective and scalable solution.

Using cloud inference for real-time product identification

While designing a camera-based product recognition and payment system, we ran into a decision of whether this should be done on the edge or the cloud. After considering several architectures, we designed a system that uploads videos of the transactions to the cloud for processing.

Our end users start a transaction by scanning the A-POP’s QR code, which triggers the A-POP to unlock and then customers grab what they want and go. Preprocessed videos of these transactions are uploaded to the cloud. Our AI-powered transaction pipeline automatically processes these videos and charges the customer’s account accordingly.

The following diagram shows the architecture of our solution.

Unlocking high-performance and cost-effective inference using AWS Inferentia

As retailers look to scale operations, cost of A-POPs becomes a consideration. At the same time, providing a seamless real-time shopping experience for end-users is paramount. Our AI/ML research team focuses on identifying the best computer vision (CV) models for our system. We were now presented with the challenge of how to simultaneously optimize the AI/ML operations for performance and cost.

We deploy our models on Amazon EC2 Inf1 instances powered by Inferentia, Amazon’s first ML silicon designed to accelerate deep learning inference workloads. Inferentia has been shown to reduce inference costs significantly. We used the AWS Neuron SDK—a set of software tools used with Inferentia—to compile and optimize our models for deployment on EC2 Inf1 instances.

The code snippet that follows shows how to compile a YOLO model with Neuron. The code works seamlessly with PyTorch and functions such as torch.jit.trace()and neuron.trace()record the model’s operations on an example input during the forward pass to build a static IR graph.

from ultralytics import YOLO
import torch_neuronx
import torch

batch_size = 1
imgsz = (640, 640)
im = torch.zeros(batch_size, 3, *imgsz).to('cpu')  # mock input

# Compiler options
half = True  # fp16
fp8 = False
dynamic = False  # dynamic batch

f = 'yolov8n.neuronx'  # output model name
neuronx_cc_args = ['--auto-cast', 'none']

if half:
    neuronx_cc_args = ['--auto-cast', 'all', '--auto-cast-type', 'fp16']
elif fp8:
    neuronx_cc_args = ['--auto-cast', 'all', '--auto-cast-type', 'fp8_e4m3']

model = torch.load('yolov8n.pt')['model']
model.eval()
model.float()
model = model.fuse()
neuronx_model = torch_neuronx.trace(
    model,
    example_inputs=im,
    compiler_args=neuronx_cc_args,
)

if dynamic:
    neuronx_model = torch_neuronx.dynamic_batch(neuronx_model)

neuronx_model.save(f)

We migrated our compute-heavy models to Inf1. By using AWS Inferentia, we achieved the throughput and performance to match our business needs. Adopting Inferentia-based Inf1 instances in the MLOps lifecycle was a key to achieving remarkable results:

1. Performance improvement: Our large computer vision models now run five times faster, achieving over 120 frames per second (FPS), allowing for seamless, real-time shopping experiences for our customers. Furthermore, the ability to process at this frame rate not only enhances transaction speed, but also enables us to feed more information into our models. This increase in data input significantly improves the accuracy of product detection within our models, further boosting the overall efficacy of our shopping systems.
2. Cost savings: We slashed inference costs. This significantly enhanced the architecture design supporting our A-POPs.

Data parallel inference was easy with AWS Neuron SDK

To improve performance of our inference workloads and extract maximum performance from Inferentia, we wanted to use all available NeuronCores in the Inferentia accelerator. Achieving this performance was easy with the built-in tools and APIs from the Neuron SDK. We used the torch.neuron.DataParallel() API. We’re currently using inf1.2xlarge which has one Inferentia accelerator with four Neuron accelerators. So we’re using torch.neuron.DataParallel() to fully use the Inferentia hardware and use all available NeuronCores. This Python function implements data parallelism at the module level on models created by the PyTorch Neuron API. Data parallelism is a form of parallelization across multiple devices or cores (NeuronCores for Inferentia), referred to as nodes. Each node contains the same model and parameters, but data is distributed across the different nodes. By distributing the data across multiple nodes, data parallelism reduces the total processing time of large batch size inputs compared to sequential processing. Data parallelism works best for models in latency-sensitive applications that have large batch size requirements.

Looking ahead: Accelerating retail transformation with foundation models and scalable deployment

As we venture into the future, the impact of foundation models on the retail industry cannot be overstated. Foundation models can make a significant difference in product labeling. The ability to quickly and accurately identify and categorize different products is crucial in a fast-paced retail environment. With modern transformer-based models, we can deploy a greater diversity of models to serve more of our AI/ML needs with higher accuracy, improving the experience for users and without having to waste time and money training models from scratch. By harnessing the power of foundation models, we can accelerate the process of labeling, enabling retailers to scale their A-POP solutions more rapidly and efficiently.

We have begun implementing Segment Anything Model (SAM), a vision transformer foundation model that can segment any object in any image (we will discuss this further in another blog post). SAM allows us to accelerate our labeling process with unparalleled speed. SAM is very efficient, able to process approximately 62 times more images than a human can manually create bounding boxes for in the same timeframe. SAM’s output is used to train a model that detects segmentation masks in transactions, opening up a window of opportunity for processing millions of images exponentially faster. This significantly reduces training time and cost for product planogram models.

Our product and AI/ML research teams are excited to be at the forefront of this transformation. The ongoing partnership with AWS and our use of Inferentia in our infrastructure will ensure that we can deploy these foundation models cost effectively. As early adopters, we’re working with the new AWS Inferentia 2-based instances. Inf2 instances are built for today’s generative AI and large language model (LLM) inference acceleration, delivering higher performance and lower costs. Inf2 will enable us to empower retailers to harness the benefits of AI-driven technologies without breaking the bank, ultimately making the retail landscape more innovative, efficient, and customer-centric.

As we continue to migrate more models to Inferentia and Inferentia2, including transformers-based foundational models, we are confident that our alliance with AWS will enable us to grow and innovate alongside our trusted cloud provider. Together, we will reshape the future of retail, making it smarter, faster, and more attuned to the ever-evolving needs of consumers.

Conclusion

In this technical traverse, we’ve highlighted our transformational journey using AWS Inferentia for its innovative AI/ML transactional processing system. This partnership has led to a five times increase in processing speed and a stunning 95 percent reduction in inference costs compared to our previous solution. It has changed the current approach of the retail industry by facilitating a real-time and seamless shopping experience.

If you’re interested in learning more about how Inferentia can help you save costs while optimizing performance for your inference applications, visit the Amazon EC2 Inf1 instances and Amazon EC2 Inf2 instances product pages. AWS provides various sample codes and getting started resources for Neuron SDK that you can find on the Neuron samples repository.

About the Authors

Matias Ponchon is the Head of Infrastructure at Intuitivo. He specializes in architecting secure and robust applications. With extensive experience in FinTech and Blockchain companies, coupled with his strategic mindset, helps him to design innovative solutions. He has a deep commitment to excellence, that’s why he consistently delivers resilient solutions that push the boundaries of what’s possible.

Jose Benitez is the Founder and Director of AI at Intuitivo, specializing in the development and implementation of computer vision applications. He leads a talented Machine Learning team, nurturing an environment of innovation, creativity, and cutting-edge technology. In 2022, Jose was recognized as an ‘Innovator Under 35’ by MIT Technology Review, a testament to his groundbreaking contributions to the field. This dedication extends beyond accolades and into every project he undertakes, showcasing a relentless commitment to excellence and innovation.

Diwakar Bansal is an AWS Senior Specialist focused on business development and go-to-market for Gen AI and Machine Learning accelerated computing services. Previously, Diwakar has led product definition, global business development, and marketing of technology products for IoT, Edge Computing, and Autonomous Driving focusing on bringing AI and Machine Learning to these domains.

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Enterprises seek to harness the potential of Machine Learning (ML) to solve complex problems and improve outcomes. Until recently, building and deploying ML models required deep levels of technical and coding skills, including tuning ML models and maintaining operational pipelines. Since its introduction in 2021, Amazon SageMaker Canvas has enabled business analysts to build, deploy, and use a variety of ML models – including tabular, computer vision, and natural language processing – without writing a line of code. This has accelerated the ability of enterprises to apply ML to use cases such as time-series forecasting, customer churn prediction, sentiment analysis, industrial defect detection, and many others.

As announced on October 5, 2023, SageMaker Canvas expanded its support of models to foundation models (FMs) – large language models used to generate and summarize content. With the October 12, 2023 release, SageMaker Canvas lets users ask questions and get responses that are grounded in their enterprise data. This ensures that results are context-specific, opening up additional use cases where no-code ML can be applied to solve business problems. For example, business teams can now formulate responses consistent with an organization’s specific vocabulary and tenets, and can more quickly query lengthy documents to get responses specific and grounded to the contents of those documents. All this content is performed in a private and secure manner, ensuring that all sensitive data is accessed with proper governance and safeguards.

To get started, a cloud administrator configures and populates Amazon Kendra indexes with enterprise data as data sources for SageMaker Canvas. Canvas users select the index where their documents are, and can ideate, research, and explore knowing that the output will always be backed by their sources-of-truth. SageMaker Canvas uses state-of-the-art FMs from Amazon Bedrock and Amazon SageMaker JumpStart. Conversations can be started with multiple FMs side-by-side, comparing the outputs and truly making generative-AI accessible to everyone.

In this post, we will review the recently released feature, discuss the architecture, and present a step-by-step guide to enable SageMaker Canvas to query documents from your knowledge base, as shown in the following screen capture.

Solution overview

Foundation models can produce hallucinations – responses that are generic, vague, unrelated, or factually incorrect. Retrieval Augmented Generation (RAG) is a frequently used approach to reduce hallucinations. RAG architectures are used to retrieve data from outside of an FM, which is then used to perform in-context learning to answer the user’s query. This ensures that the FM can use data from a trusted knowledge base and use that knowledge to answer users’ questions, reducing the risk of hallucination.

With RAG, the data external to the FM and used to augment user prompts can come from multiple disparate data sources, such as document repositories, databases, or APIs. The first step is to convert your documents and any user queries into a compatible format to perform relevancy semantic search. To make the formats compatible, a document collection, or knowledge library, and user-submitted queries are converted into numerical representations using embedding models.

With this release, RAG functionality is provided in a no-code and seamless manner. Enterprises can enrich the chat experience in Canvas with Amazon Kendra as the underlying knowledge management system. The following diagram illustrates the solution architecture.

Connecting SageMaker Canvas to Amazon Kendra requires a one-time set-up. We describe the set-up process in detail in Setting up Canvas to query documents. If you haven’t already set-up your SageMaker Domain, refer to Onboard to Amazon SageMaker Domain.

As part of the domain configuration, a cloud administrator can choose one or more Kendra indices that the business analyst can query when interacting with the FM through SageMaker Canvas.

After the Kendra indices are hydrated and configured, business analysts use them with SageMaker Canvas by starting a new chat and selecting “Query Documents” toggle. SageMaker Canvas will then manage the underlying communication between Amazon Kendra and the FM of choice to perform the following operations:

1. Query the Kendra indices with the question coming from the user.
2. Retrieve the snippets (and the sources) from Kendra indices.
3. Engineer the prompt with the snippets with the original query so that the foundation model can generate an answer from the retrieved documents.
4. Provide the generated answer to the user, along with references to the pages/documents that were used to formulate the response.

Setting up Canvas to query documents

In this section, we will walk you through the steps to set up Canvas to query documents served through Kendra indexes. You should have the following prerequisites:

• SageMaker Domain setup – Onboard to Amazon SageMaker Domain
• Create a Kendra index (or more than one)
• Setup the Kendra Amazon S3 connector – follow the Amazon S3 Connector – and upload PDF files and other documents to the Amazon S3 bucket associated with the Kendra index
• Setup IAM so that Canvas has the appropriate permissions, including those required for calling Amazon Bedrock and/or SageMaker endpoints – follow the Set-up Canvas Chat documentation

Now you can update the Domain so that it can access the desired indices. On the SageMaker console, for the given Domain, select Edit under the Domain Settings tab. Enable the toggle “Enable query documents with Amazon Kendra” which can be found at the Canvas Settings step. Once activated, choose one or more Kendra indices that you want to use with Canvas. Once activated, choose one or more Kendra indices that you want to use with Canvas.

That’s all that’s needed to configure Canvas query documents feature. Users can now jump into a chat within Canvas and start using the knowledge bases that have been attached to the Domain through the Kendra indexes. The maintainers of the knowledge-base can continue to update the source-of-truth and with the syncing capability in Kendra, the chat users will automatically be able to use the up-to-date information in a seamless manner.

Using the Query Documents feature for chat

As a SageMaker Canvas user, the Query Documents feature can be accessed from within a chat. To start the chat session, click or search for the “Generate, extract and summarize content” button from the Ready-to-use models tab in SageMaker Canvas.

Once there, you can turn on and off Query Documents with the toggle at the top of the screen. Check out the information prompt to learn more about the feature.

When Query Documents is enabled, you can choose among a list of Kendra indices enabled by the cloud administrator.

You can select an index when starting a new chat. You can then ask a question in the UX with knowledge being automatically sourced from the selected index. Note that after a conversation has started against a specific index, it is not possible to switch to another index.

For the questions asked, the chat will show the answer generated by the FM along with the source documents that contributed to generating the answer. When clicking any of the source documents, Canvas opens a preview of the document, highlighting the excerpt used by the FM.

Conclusion

Conversational AI has immense potential to transform customer and employee experience by providing a human-like assistant with natural and intuitive interactions such as:

• Performing research on a topic or search and browse the organization’s knowledge base
• Summarizing volumes of content to rapidly gather insights
• Searching for Entities, Sentiments, PII and other useful data, and increasing the business value of unstructured content
• Generating drafts for documents and business correspondence
• Creating knowledge articles from disparate internal sources (incidents, chat logs, wikis)

The innovative integration of chat interfaces, knowledge retrieval, and FMs enables enterprises to provide accurate, relevant responses to user questions by using their domain knowledge and sources-of-truth.

By connecting SageMaker Canvas to knowledge bases in Amazon Kendra, organizations can keep their proprietary data within their own environment while still benefiting from state-of-the-art natural language capabilities of FMs. With the launch of SageMaker Canvas’s Query Documents feature, we are making it easy for any enterprise to use LLMs and their enterprise knowledge as source-of-truth to power a secure chat experience. All this functionality is available in a no-code format, allowing businesses to avoid handling the repetitive and non-specialized tasks.

To learn more about SageMaker Canvas and how it helps make it easier for everyone to start with Machine Learning, check out the SageMaker Canvas announcement. Learn more about how SageMaker Canvas helps foster collaboration between data scientists and business analysts by reading the Build, Share & Deploy post. Finally, to learn how to create your own Retrieval Augmented Generation workflow, refer to SageMaker JumpStart RAG.

References

Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W., Rocktäschel, T., Riedel, S., Kiela, D. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. Advances in Neural Information Processing Systems, 33, 9459-9474.

About the Authors

Davide Gallitelli is a Senior Specialist Solutions Architect for AI/ML. He is based in Brussels and works closely with customers all around the globe that are looking to adopt Low-Code/No-Code Machine Learning technologies, and Generative AI. 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.

Bilal Alam is an Enterprise Solutions Architect at AWS with a focus on the Financial Services industry. On most days Bilal is helping customers with building, uplifting and securing their AWS environment to deploy their most critical workloads. He has extensive experience in Telco, networking, and software development. More recently, he has been looking into using AI/ML to solve business problems.

Pashmeen Mistry is a Senior Product Manager at AWS. Outside of work, Pashmeen enjoys adventurous hikes, photography, and spending time with his family.

Dan Sinnreich is a Senior Product Manager at AWS, helping to democratize low-code/no-code machine learning. Previous to AWS, Dan built and commercialized enterprise SaaS platforms and time-series models used by institutional investors to manage risk and construct optimal portfolios. Outside of work, he can be found playing hockey, scuba diving, and reading science fiction.

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