Posts in category: Amazon AWS

Amazon Kendra is a highly accurate and simple-to-use intelligent search service powered by machine learning (ML). Amazon Kendra offers a suite of data source connectors to simplify the process of ingesting and indexing your content, wherever it resides.

Valuable data in organizations is stored in both structured and unstructured repositories. An enterprise search solution should be able to index and search across several structured and unstructured repositories.

Alfresco Content Services provides open, flexible, highly scalable enterprise content management (ECM) capabilities with the added benefits of a content services platform, making content accessible wherever and however you work through easy integrations with the business applications you use every day. Many organizations use the Alfresco content management platform to store their content. One of the key requirements for enterprise customers using Alfresco is the ability to easily and securely find accurate information across all the stored documents.

We are excited to announce that you can now use the new Amazon Kendra Alfresco connector to search documents stored in your Alfresco repositories and sites. In this post, we show how to use the new connector to retrieve documents stored in Alfresco for indexing purposes and securely use the Amazon Kendra intelligent search function. In addition, the ML-powered intelligent search can accurately find information from unstructured documents with natural language narrative content, for which keyword search is not very effective.

What’s new in the Amazon Kendra Alfresco connector

The Amazon Kendra Alfresco connector offers support for the following:

• Basic and OAuth2 authentication mechanisms for the Alfresco On-Premises (On-Prem) platform
• Basic and OAuth2 authentication mechanisms for the Alfresco PaaS platform
• Aspect-based crawling of Alfresco repository documents

Solution overview

With Amazon Kendra, you can configure multiple data sources to provide a central place to search across your document repositories and sites. The solution in this post demonstrates the following:

• Retrieval of documents and comments from Alfresco private sites and public sites
• Retrieval of documents and comments from Alfresco repositories using Amazon Kendra-specific aspects
• Authentication against Alfresco On-Prem and PaaS platforms using Basic and OAuth2 mechanisms, respectively
• The Amazon Kendra search capability with access control across sites and repositories

If you are going to use only one of the platforms, you can still follow this post to build the example solution; just ignore the steps corresponding to the platform that you are not using.

The following is a summary of the steps to build the example solution:

1. Upload documents to the three Alfresco sites and the repository folder. Make sure the uploaded documents are unique across sites and repository folders.
2. For the two private sites and repository, use document-level Alfresco permission management to set access permissions. For the public site, you don’t need to set up permissions at the document level. Note that permissions information is retrieved by the Amazon Kendra Alfresco connector and used for access control by the Amazon Kendra search function.
3. For the two private sites and repository, create a new Amazon Kendra index (you use the same index for both the private sites and the repository). For the public site, create a new Amazon Kendra index.
4. For the On-Prem private site, create an Amazon Kendra Alfresco data source using Basic authentication, within the Amazon Kendra index for private sites.
5. For the On-Prem repository documents with Amazon Kendra-specific aspects, create a data source using Basic authentication, within the Amazon Kendra index for private sites.
6. For the PaaS private site, create a data source using Basic authentication, within the Amazon Kendra index for private sites.
7. For the PaaS public site, create a data source using OAuth2 authentication, within the Amazon Kendra index for public sites.
8. Perform a sync for each data source.
9. Run a test query in the Amazon Kendra index meant for private sites and the repository using access control.
10. Run a test query in the Amazon Kendra index meant for public sites without access control.

Prerequisites

You need 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. You need to have a basic knowledge of AWS and how to navigate the AWS Management Console.

For the Alfresco On-Prem platform, complete the following steps:

1. Create a private site or use an existing site.
2. Create a repository folder or use an existing repository folder.
3. Get the repository URL.
4. Get Basic authentication credentials (user ID and password).
5. Make sure authentication are part of the ALFRESCO_ADMINISTRATORS group.
6. Get the public X509 certificate in .pem format and save it locally.

For the Alfresco PaaS platform, complete the following steps:

1. Create a private site or use an existing site.
2. Create a public site or use an existing site.
3. Get the repository URL.
4. Get Basic authentication credentials (user ID and password).
5. Get OAuth2 credentials (client ID, client secret, and token URL).
6. Confirm that authentication users are part of the ALFRESCO_ADMINISTRATORS group.

Step 1: Upload example documents

Each uploaded document must have 5 MB or less in text. For more information, see Amazon Kendra Service Quotas. You can upload example documents or use existing documents within each site.

As shown in the following screenshot, we have uploaded four documents to the Alfresco On-Prem private site.

We have uploaded three documents to the Alfresco PaaS private site.

We have uploaded five documents to the Alfresco PaaS public site.

We have uploaded two documents to the Alfresco On-Prem repository.

Assign the aspect awskendra:indexControl to one or more documents in the repository folder.

Step 2: Configure Alfresco permissions

Use the Alfresco Permissions Management feature to give access rights to example users for viewing uploaded documents. It is assumed that you have some example Alfresco user names, with email addresses, that can be used for setting permissions at the document level in private sites. These users are not used for crawling the sites.

In the following example for the On-Prem private site, we have provided users My Dev User1 and My Dev User2 with site-consumer access to the example document. Repeat the same procedure for the other uploaded documents.

In the following example for the PaaS private site, we have provided user Kendra User 3 with site-consumer access to the example document. Repeat the same procedure for the other uploaded documents.

For the Alfresco repository documents, we have provided user My Dev user1 with consumer access to the example document.

The following table lists the site or repository names, document names, and permissions.

Step 3: Set up Amazon Kendra indexes

You can create a new Amazon Kendra index or use an existing index for indexing documents hosted in Alfresco private sites. To create a new index, complete the following steps:

1. On the Amazon Kendra console, create an index called Alfresco-Private.
2. Create a new IAM role, then choose Next.
3. For Access Control, choose Yes.
4. For Token Type¸ choose JSON.
5. Keep the user name and group as default.
6. Choose None for user group expansion because we are assuming no integration with AWS IAM Identity Center (successor to AWS Single Sign-On).
7. Choose Next.
8. Choose Developer Edition for this example solution.
9. Choose Create to create a new index.

The following screenshot shows the Alfresco-Private index after it has been created.

10. You can verify the access control configuration on the User access control tab.

11. Repeat these steps to create a second index called Alfresco-Public.

Step 4: Create a data source for the On-Prem private site

To create a data source for the On-Prem private site, complete the following steps:

1. On the Amazon Kendra console, navigate to the Alfresco-Private index.
2. Choose Data sources in the navigation pane.
3. Choose Add data source.

4. Choose Add connector for the Alfresco connector.

5. For Data source name, enter Alfresco-OnPrem-Private.
6. Optionally, add a description.
7. Keep the remaining settings as default and choose Next.

To connect to the Alfresco On-Prem site, the connector needs access to the public certificate corresponding to the On-Prem server. This was one of the prerequisites.

8. Use a different browser tab to upload the .pem file to an Amazon Simple Storage Service (Amazon S3) bucket in your account.

You use this S3 bucket name in the next steps.

9. Return to the data source creation page.
10. For Source, select Alfresco server.
11. For Alfresco repository URL, enter the repository URL (created as a prerequisite).
12. For Alfresco user application URL, enter the same value as the repository URL.
13. For SSL certificate location, choose Browse S3 and choose the S3 bucket where you uploaded the .pem file.
14. For Authentication, select Basic authentication.
15. For AWS Secrets Manager secret, choose Create and add new secret.

A pop-up window opens to create an AWS Secrets Manager secret.

16. Enter a name for your secret, user name, and password, then choose Save.

17. For Virtual Private Cloud (VPC), choose No VPC.
18. Turn the identity crawler on.
19. For IAM role, choose Create a new IAM role.
20. Choose Next.

You can configure the data source to synchronize contents from one or more Alfresco sites. For this post, we sync to the on-prem private site.

21. For Content to sync, select Single Alfresco site sync and choose MyAlfrescoSite.
22. Select Include comments to retrieve comments in addition to documents.
23. For Sync mode, select Full sync.
24. For Frequency, choose Run on demand (or a different frequency option as needed).
25. Choose Next.

26. Map the Alfresco document fields to the Amazon Kendra index fields (you can keep the defaults), then choose Next.

27. On the Review and Create page, verify all the information, then choose Add data source.

After the data source has been created, the data source page is displayed as shown in the following screenshot.

Step 5: Create a data source for the On-Prem repository documents with Amazon Kendra-specific aspects

Similarly to the previous steps, create a data source for the On-Prem repository documents with Amazon Kendra-specific aspects:

1. On the Amazon Kendra console, navigate to the Alfresco-Private index.
2. Choose Data sources in the navigation pane.
3. Choose Add data source.
4. Choose Add connector for the Alfresco connector.
5. For Data source name, enter Alfresco-OnPrem-Aspects.
6. Optionally, add a description.
7. Keep the remaining settings as default and choose Next.
8. For Source, select Alfresco server.
9. For Alfresco repository URL, enter the repository URL (created as a prerequisite).
10. For Alfresco user application URL, enter the same value as the repository URL.
11. For SSL certificate location, choose Browse S3 and choose the S3 bucket where you uploaded the .pem file.
12. For Authentication, select Basic authentication.
13. For AWS Secrets Manager secret, choose the secret you created earlier.
14. For Virtual Private Cloud (VPC), choose No VPC.
15. Turn the identity crawler off.
16. For IAM role, choose Create a new IAM role.
17. Choose Next.

For this scope, the connector retrieves only those On-Prem server repository documents that have been assigned an aspect called awskendra:indexControl.

18. For Content to sync, select Alfresco aspects sync.
19. For Sync mode, select Full sync.
20. For Frequency, choose Run on demand (or a different frequency option as needed).
21. Choose Next.
22. Map the Alfresco document fields to the Amazon Kendra index fields (you can keep the defaults), then choose Next.
23. On the Review and Create page, verify all the information, then choose Add data source.

After the data source has been created, the data source page is displayed as shown in the following screenshot.

Step 6: Create a data source for the PaaS private site

Follow similar steps as the previous sections to create a data source for the PaaS private site:

1. On the Amazon Kendra console, navigate to the Alfresco-Private index.
2. Choose Data sources in the navigation pane.
3. Choose Add data source.
4. Choose Add connector for the Alfresco connector.
5. For Data source name, enter Alfresco-Cloud-Private.
6. Optionally, add a description.
7. Keep the remaining settings as default and choose Next.
8. For Source, select Alfresco cloud.
9. For Alfresco repository URL, enter the repository URL (created as a prerequisite).
10. For Alfresco user application URL, enter the same value as the repository URL.
11. For Authentication, select Basic authentication.
12. For AWS Secrets Manager secret, choose Create and add new secret.
13. Enter a name for your secret, user name, and password, then choose Save.
14. For Virtual Private Cloud (VPC), choose No VPC.
15. Turn the identity crawler off.
16. For IAM role, choose Create a new IAM role.
17. Choose Next.

We can configure the data source to synchronize contents from one or more Alfresco sites. For this post, we configure the data source to sync from the PaaS private site MyAlfrescoCloudSite2.

18. For Content to sync, select Single Alfresco site sync and choose MyAlfrescoCloudSite2.
19. Select Include comments.
20. For Sync mode, select Full sync.
21. For Frequency, choose Run on demand (or a different frequency option as needed).
22. Choose Next.
23. Map the Alfresco document fields to the Amazon Kendra index fields (you can keep the defaults) and choose Next.
24. On the Review and Create page, verify all the information, then choose Add data source.

After the data source has been created, the data source page is displayed as shown in the following screenshot.

Step 7: Create a data source for the PaaS public site

We follow similar steps as before to create a data source for the PaaS public site:

1. On the Amazon Kendra console, navigate to the Alfresco-Public index.
2. Choose Data sources in the navigation pane.
3. Choose Add data source.
4. Choose Add connector for the Alfresco connector.
5. For Data source name, enter Alfresco-Cloud-Public.
6. Optionally, add a description.
7. Keep the remaining settings as default and choose Next.
8. For Source, select Alfresco cloud.
9. For Alfresco repository URL, enter the repository URL (created as a prerequisite).
10. For Alfresco user application URL, enter the same value as the repository URL.
11. For Authentication, select OAuth2.0 authentication.
12. For AWS Secrets Manager secret, choose Create and add new secret.
13. Enter a name for your secret, client ID, client secret, and token URL, then choose Save.
14. For Virtual Private Cloud (VPC), choose No VPC.
15. Turn the identity crawler off.
16. For IAM role, choose Create a new IAM role.
17. Choose Next.

We configure this data source to sync to the PaaS public site MyAlfrescoCloudPublicSite.

18. For Content to sync, select Single Alfresco site sync and choose MyAlfrescoCloudPublicSite.
19. Optionally, select Include comments.
20. For Sync mode, select Full sync.
21. For Frequency, choose Run on demand (or a different frequency option as needed).
22. Choose Next.
23. Map the Alfresco document fields to the Amazon Kendra index fields (you can keep the defaults) and choose Next.
24. On the Review and Create page, verify all the information, then choose Add data source.

After the data source has been created, the data source page is displayed as shown in the following screenshot.

Step 8: Perform a sync for each data source

Navigate to each of the data sources and choose Sync now. Complete only one synchronization at a time.

Wait for synchronization to be complete for all data sources. When each synchronization is complete for a data source, you see the status as shown in the following screenshot.

You can also view Amazon CloudWatch logs for a specific sync under Sync run history.

Step 9: Run a test query in the private index using access control

Now it’s time to test the solution. We first run a query in the private index using access control:

1. On the Amazon Kendra console, navigate to the Alfresco-Private index and choose Search indexed content.

2. Enter a query in the search field.

As shown in the following screenshot, Amazon Kendra didn’t return any results.

3. Choose Apply token.
4. Enter the email address corresponding to the My Dev User1 user and choose Apply.

Note that Amazon Kendra access control works based on the email address associated with an Alfresco user name.

5. Run the search again.

The search results in a document list (containing wellarchitected-sustainability-pillar.pdf in the following example) based on the access control setup.

If you run the same query again and provide an email address that doesn’t have access to either of these documents, you should not see these documents in the results list.

6. Enter another query to search in the documents based on the aspect awskendra:indexControl.
7. Choose Apply token, enter the email address corresponding to My Dev User1 user, and choose Apply.
8. Rerun the query.

Step 10: Run a test query in the public index without access control.

Similarly, we can test our solution by running queries in the public index without access control:

1. On the Amazon Kendra console, navigate to the Alfresco-Public index and choose Search indexed content.
2. Run a search query.

Because this example Alfresco public site has not been set up with any access control, we don’t use an access token.

Clean up

To avoid incurring future costs, clean up the resources you created as part of this solution. Delete newly added Alfresco data sources within the indexes. If you created new Amazon Kendra indexes while testing this solution, delete them as well.

Conclusion

With the new Alfresco connector for Amazon Kendra, organizations can tap into the repository of information stored in their account securely using intelligent search powered by Amazon Kendra.

To learn about these possibilities and more, refer to the Amazon Kendra Developer Guide. For more information on how you can create, modify, or delete metadata and content when ingesting your data from Alfresco, refer to Enriching your documents during ingestion and Enrich your content and metadata to enhance your search experience with custom document enrichment in Amazon Kendra.

About the Authors

Arun Anand is a Senior Solutions Architect at Amazon Web Services based in Houston area. He has 25+ years of experience in designing and developing enterprise applications. He works with partners in Energy & Utilities segment providing architectural and best practice recommendations for new and existing solutions.

Rajnish Shaw is a Senior Solutions Architect at Amazon Web Services, with a background as a Product Developer and Architect. Rajnish is passionate about helping customers build applications on the cloud. Outside of work Rajnish enjoys spending time with family and friends, and traveling.

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.

Read More

This post is co-authored by Daryl Martis, Director of Product, Salesforce Einstein AI.

This is the second post in a series discussing the integration of Salesforce Data Cloud and Amazon SageMaker. In Part 1, we show how the Salesforce Data Cloud and Einstein Studio integration with SageMaker allows businesses to access their Salesforce data securely using SageMaker and use its tools to build, train, and deploy models to endpoints hosted on SageMaker. The endpoints are then registered to the Salesforce Data Cloud to activate predictions in Salesforce.

In this post, we expand on this topic to demonstrate how to use Einstein Studio for product recommendations. You can use this integration for traditional models as well as large language models (LLMs).

Solution overview

In this post, we demonstrate how to create a predictive model in SageMaker to recommend the next best product to your customers by using historical data such as customer demographics, marketing engagements, and purchase history from Salesforce Data Cloud.

We use the following sample dataset. To use this dataset in your Data Cloud, refer to Create Amazon S3 Data Stream in Data Cloud.

The following attributes are needed to create the model:

• Club Member – If the customer is a club member
• Campaign – The campaign the customer is a part of
• State – The state or province the customer resides in
• Month – The month of purchase
• Case Count – The number of cases raised by the customer
• Case Type Return – Whether the customer returned any product within the last year
• Case Type Shipment Damaged – Whether the customer had any shipments damaged in the last year
• Engagement Score – The level of engagement the customer has (response to mailing campaigns, logins to the online store, and so on)
• Tenure – The tenure of the customer relationship with the company
• Clicks – The average number of clicks the customer has made within a week prior to purchase
• Pages Visited – The average number of pages the customer has visited within a week prior to purchase
• Product Purchased – The actual product purchased
• Id – The ID of the record
• DateTime – The timestamp of the dataset

The product recommendation model is built and deployed on SageMaker and is trained using data in the Salesforce Data Cloud. The following steps give an overview of how to use the new capabilities launched in SageMaker for Salesforce to enable the overall integration:

1. Set up the Amazon SageMaker Studio domain and OAuth between Salesforce and the AWS accounts.
2. Use the newly launched capability of the Amazon SageMaker Data Wrangler connector for Salesforce Data Cloud to prepare the data in SageMaker without copying the data from Salesforce Data Cloud.
3. Train a recommendation model in SageMaker Studio using training data that was prepared using SageMaker Data Wrangler.
4. Package the SageMaker Data Wrangler container and the trained recommendation model container in an inference pipeline so the inference request can use the same data preparation steps you created to preprocess the training data. The real-time inference call data is first passed to the SageMaker Data Wrangler container in the inference pipeline, where it is preprocessed and passed to the trained model for product recommendation. For more information about this process, refer to New — Introducing Support for Real-Time and Batch Inference in Amazon SageMaker Data Wrangler. Although we use a specific algorithm to train the model in our example, you can use any algorithm that you find appropriate for your use case.
5. Use the newly launched SageMaker provided project template for Salesforce Data Cloud integration to streamline implementing the preceding steps by providing the following templates:
   1. An example notebook showcasing data preparation, building, training, and registering the model.
   2. The SageMaker provided project template for Salesforce Data Cloud integration, which automates creating a SageMaker endpoint hosting the inference pipeline model. When a version of the model in the Amazon SageMaker Model Registry is approved, the endpoint is exposed as an API with Amazon API Gateway using a custom Salesforce JSON Web Token (JWT) authorizer. API Gateway is required to allow Salesforce Data Cloud to make predictions against the SageMaker endpoint using a JWT token that Salesforce creates and passes with the request when making predictions from Salesforce. JWT can be used as a part of OpenID Connect (OIDC) and OAuth 2.0 frameworks to restrict client access to your APIs.
6. After you create the API, we recommend registering the model endpoint in Salesforce Einstein Studio. For instructions, refer to Bring Your Own AI Models to Salesforce with Einstein Studio

The following diagram illustrates the solution architecture.

Create a SageMaker Studio domain

First, create a SageMaker Studio domain. For instructions, refer to Onboard to Amazon SageMaker Domain. You should note down the domain ID and execution role that is created and will be used by your user profile. You add permissions to this role in subsequent steps.

The following screenshot shows the domain we created for this post.

The following screenshot shows the example user profile for this post.

Set up the Salesforce connected app

Next, we create a Salesforce connected app to enable the OAuth flow from SageMaker Studio to Salesforce Data Cloud. Complete the following steps:

1. Log in to Salesforce and navigate to Setup.
2. Search for App Manager and create a new connected app.
3. Provide the following inputs:
   1. For Connected App Name, enter a name.
   2. For API Name, leave as default (it’s automatically populated).
   3. For Contact Email, enter your contact email address.
   4. Select Enable OAuth Settings.
   5. For Callback URL, enter https://<domain-id>.studio.<region>.sagemaker.aws/jupyter/default/lab, and provide the domain ID that you captured while creating the SageMaker domain and the Region of your SageMaker domain.
      
4. Under Selected OAuth Scopes, move the following from Available OAuth Scopes to Selected OAuth Scopes and choose Save:
   1. Manage user data via APIs (api)
   2. Perform requests at any time (refresh_token, offline_access)
   3. Perform ANSI SQL queries on Salesforce Data Cloud data (Data Cloud_query_api)
   4. Manage Salesforce Customer Data Platform profile data (Data Cloud_profile_api
   5. Access the identity URL service (id, profile, email, address, phone)
   6. Access unique user identifiers (openid)

For more information about creating a connected app, refer to Create a Connected App.

5. Return to the connected app and navigate to Consumer Key and Secret.
6. Choose Manage Consumer Details.
   
7. Copy the key and secret.

You may be asked to log in to your Salesforce org as part of the two-factor authentication here.

8. Navigate back to the Manage Connected Apps page.
9. Open the connected app you created and choose Manage.
10. Choose Edit Policies and change IP Relaxation to Relax IP restrictions, then save your settings.

Configure SageMaker permissions and lifecycle rules

In this section, we walk through the steps to configure SageMaker permissions and lifecycle management rules.

Create a secret in AWS Secrets Manager

Enable OAuth integration with Salesforce Data Cloud by storing credentials from your Salesforce connected app in AWS Secrets Manager:

1. On the Secrets Manager console, choose Store a new secret.
2. Select Other type of secret.
3. Create your secret with the following key-value pairs:

   {
   "identity_provider": "SALESFORCE",
   "authorization_url": "https://login.salesforce.com/services/oauth2/authorize",
   "token_url": "https://login.salesforce.com/services/oauth2/token",
   "client_id": "<YOUR_CONSUMER_KEY>",
   "client_secret": "<YOUR_CONSUMER_SECRET>"
   “issue_url”: “<YOUR_SALESFORCE_ORG_URL>”
   }

   

4. Add a tag with the key sagemaker:partner and your choice of value.
5. Save the secret and note the ARN of the secret.

Configure a SageMaker lifecycle rule

The SageMaker Studio domain execution role will require AWS Identity and Access Management (IAM) permissions to access the secret created in the previous step. For more information, refer to Creating roles and attaching policies (console).

1. On the IAM console, attach the following polices to their respective roles (these roles will be used by the SageMaker project for deployment):
   1. Add the policy AmazonSageMakerPartnerServiceCatalogProductsCloudFormationServiceRolePolicy to the service role AmazonSageMakerServiceCatalogProductsCloudformationRole.
   2. Add the policy AmazonSageMakerPartnerServiceCatalogProductsApiGatewayServiceRolePolicy to the service role AmazonSageMakerServiceCatalogProductsApiGatewayRole.
   3. Add the policy AmazonSageMakerPartnerServiceCatalogProductsLambdaServiceRolePolicy to the service role AmazonSageMakerServiceCatalogProductsLambdaRole.
2. On the IAM console, navigate to the SageMaker domain execution role.
3. Choose Add permissions and select Create an inline policy.
4. Enter the following policy in the JSON policy editor:

   {
   "Version": "2012-10-17",
   "Statement": [
   {
   "Effect": "Allow",
   "Action": [
   "secretsmanager:GetSecretValue",
   "secretsmanager:PutSecretValue"
   ],
   "Resource": "arn:aws:secretsmanager:*:*:secret:*",
   "Condition": {
   "ForAnyValue:StringLike": {
   "aws:ResourceTag/sagemaker:partner": "*"
   }
   }
   },
   {
   "Effect": "Allow",
   "Action": [
   "secretsmanager:UpdateSecret"
   ],
   "Resource": "arn:aws:secretsmanager:*:*:secret:AmazonSageMaker-*"
   }
   ]
   }

SageMaker Studio lifecycle configuration provides shell scripts that run when a notebook is created or started. The lifecycle configuration will be used to retrieve the secret and import it to the SageMaker runtime.

5. On the SageMaker console, choose Lifecycle configurations in the navigation pane.
6. Choose Create configuration.
7. Leave the default selection Jupyter Server App and choose Next.
8. Give the configuration a name.
9. Enter the following script in the editor, providing the ARN for the secret you created earlier:

   #!/bin/bash
   set -eux
   
   cat > ~/.sfgenie_identity_provider_oauth_config <<EOL
   {
   "secret_arn": "<YOUR_SECRETS_ARN>"
   }
   EOL

10. Choose Submit to save the lifecycle configuration.
11. Choose Domains in the navigation pane and open your domain.
12. On the Environment tab, choose Attach to attach your lifecycle configuration.
13. Choose the lifecycle configuration you created and choose Attach to domain.
14. Choose Set as default.

If you are a returning user to SageMaker Studio, in order to ensure Salesforce Data Cloud is enabled, upgrade to the latest Jupyter and SageMaker Data Wrangler kernels.

This completes the setup to enable data access from Salesforce Data Cloud to SageMaker Studio to build AI and machine learning (ML) models.

Create a SageMaker project

To start using the solution, first create a project using Amazon SageMaker Projects. Complete the following steps:

1. In SageMaker Studio, under Deployments in the navigation pane, choose Projects.
2. Choose Create project.
3. Choose the project template called Model deployment for Salesforce.
4. Choose Select project template.
   
5. Enter a name and optional description for your project.
6. Enter a model group name.
7. Enter the name of the Secrets Manager secret that you created earlier.
8. Choose Create project.

The project may take 1–2 minutes to initiate.

You can see two new repositories. The first one is for sample notebooks that you can use as is or customize to prepare, train, create, and register models in the SageMaker Model Registry. The second repository is for automating the model deployment, which includes exposing the SageMaker endpoint as an API.

9. Choose clone repo for both notebooks.

For this post, we use the product recommendation example, which can be found in the sagemaker-<YOUR-PROJECT-NAME>-p-<YOUR-PROJECT-ID>-example-nb/product-recommendation directory that you just cloned. Before we run the product-recommendation.ipynb notebook, let’s do some data preparation to create the training data using SageMaker Data Wrangler.

Prepare data with SageMaker Data Wrangler

Complete the following steps:

1. In SageMaker Studio, on the File menu, choose New and Data Wrangler flow.
   
2. After you create the data flow, choose (right-click) the tab and choose Rename to rename the file.
3. Choose Import data.
   
4. Choose Create connection.
   
5. Choose Salesforce Data Cloud.
   
6. For Name, enter salesforce-data-cloud-sagemaker-connection.
7. For Salesforce org URL, enter your Salesforce org URL.
8. Choose Save + Connect.
   
9. In the Data Explorer view, select and preview the tables from the Salesforce Data Cloud to create and run the query to extract the required dataset.
10. Your query will look like below and you may use the table name that you used while uploading data in Salesforce Data Cloud.

    SELECT product_purchased__c, club_member__c, campaign__c, state__c, month__c,
          case_count__c,case_type_return__c, case_type_shipment_damaged__c,
          pages_visited__c,engagement_score__c, tenure__c, clicks__c, id__c
    FROM Training_Dataset_for_Sagemaker__dll

11. Choose Create dataset.

Creating the dataset may take some time.

In the data flow view, you can now see a new node added to the visual graph.

For more information on how you can use SageMaker Data Wrangler to create Data Quality and Insights Reports, refer to Get Insights On Data and Data Quality.

SageMaker Data Wrangler offers over 300 built-in transformations. In this step, we use some of these transformations to prepare the dataset for an ML model. For detailed instructions on how to implement these transformations, refer to Transform Data.

12. Use the Manage columns step with the Drop column transform to drop the column id__c.
    
13. Use the Handle missing step with the Drop missing transform to drop rows with missing values for various features. We apply this transformation on all columns.
    
14. Use a custom transform step to create categorical values for state__c, case_count__c, and tenure features. Use the following code for this transformation:

    from pyspark.sql.functions import when
     
    States_List = [‘Washington’, ‘Massachusetts’, ‘California’, ‘Minnesota’, ‘Vermont’, ‘Colorado’, ‘Arizona’]
     
    df.withColumn(“club_member__c”,df.club_member__c.cast(‘string’))
    df.withColumn(“month__c”,df.month__c.cast(‘string’))
    df.withColumn(“case_type_return__c”,df.case_type_return__c.cast(‘string’))
    df.withColumn(“case_type_shipment_damaged__c”,df.case_type_shipment_damaged__c.cast(‘string’))
     
    df = df.withColumn(‘state__c’, when(df.state__c.isin(States_List), df.state__c).otherwise(“Other”))
     
    df = df.withColumn(‘case_count__c’, when(df.case_count__c == 0, “No Cases”).otherwise( when(df.case_count__c <= 2, “1 to 2 Cases”).otherwise(“Greater than 2 Cases”)))
                      
    df = df.withColumn(‘tenure__c’, when(df.tenure__c < 1, “Less than 1 Year”).otherwise( when(df.tenure__c == 1, “1 to 2 Years”).otherwise(when(df.tenure__c ==2, “2 to 3 Years”).otherwise(when(df.tenure__c == 3, “3 to 4 Years”).otherwise(“Grater Than 4 Years”)))))

    

15. Use the Process numeric step with the Scale values transform and choose Standard scaler to scale clicks__c, engagement__score, and pages__visited__c features.
    
16. Use the Encode categorical step with the One-hot encode transform to convert categorical variables to numeric for case__type__return___c, case__type_shipment__damaged, month__c, club__member__c, and campaign__c features (all features except clicks__c, engagement__score, pages__visited__c, and product_purchased__c).
    

Model building, training, and deployment

To build, train, and deploy the model, complete the following steps:

1. Return to the SageMaker project, open the product-recommendation.ipynb notebook, and run a processing job to preprocess the data using the SageMaker Data Wrangler configuration you created.
2. Follow the steps in the notebook to train a model and register it to the SageMaker Model Registry.
   
3. Make sure to update the model group name to match with the model group name that you used while creating the SageMaker project.

To locate the model group name, open the SageMaker project that you created earlier and navigate to the Settings tab.

Similarly, the flow file referenced in the notebook must match with the flow file name that you created earlier.

4. For this post, we used product-recommendation as the model group name, so we update the notebook with project-recommendation as the model group name in the notebook.

After the notebook is run, the trained model is registered in the Model Registry. To learn more about the Model Registry, refer to Register and Deploy Models with Model Registry.

5. Select the model version you created and update the status of it to Approved.

Now that you have approved the registered model, the SageMaker Salesforce project deploy step will provision and trigger AWS CodePipeline.

CodePipeline has steps to build and deploy a SageMaker endpoint for inference containing the SageMaker Data Wrangler preprocessing steps and the trained model. The endpoint will be exposed to Salesforce Data Cloud as an API through API Gateway. The following screenshot shows the pipeline prefixed with Sagemaker-salesforce-product-recommendation-xxxxx. We also show you the endpoints and API that gets created by the SageMaker project for Salesforce.

If you would like, you can take a look at the CodePipeline deploy step, which uses AWS CloudFormation scripts to create SageMaker endpoint and API Gateway with a custom JWT authorizer.

When pipeline deployment is complete, you can find the SageMaker endpoint on the SageMaker console.

You can explore the API Gateway created by the project template on the API Gateway console.

Choose the link to find the API Gateway URL.

You can find the details of the JWT authorizer by choosing Authorizers on the API Gateway console. You can also go to the AWS Lambda console to review the code of the Lambda function created by project template.

To discover the schema to be used while invoking the API from Einstein Studio, choose Information in the navigation pane of the Model Registry. You will see an Amazon Simple Storage Service (Amazon S3) link to a metadata file. Copy and paste the link into a new browser tab URL.

Let’s look at the file without downloading it. On the file details page, choose the Object actions menu and choose Query with S3 Select.

Choose Run SQL query and take note of the API Gateway URL and schema because you will need this information when registering with Einstein Studio. If you don’t see an APIGWURL key, either the model wasn’t approved, deployment is still in progress, or deployment failed.

Use the Salesforce Einstein Studio API for predictions

Salesforce Einstein Studio is a new and centralized experience in Salesforce Data Cloud that data science and engineering teams can use to easily access their traditional models and LLMs used in generative AI. Next, we set up the API URL and client_id that you set in Secrets Manager earlier in Salesforce Einstein Studio to register and use the model inferences in Salesforce Einstein Studio. For instructions, refer to Bring Your Own AI Models to Salesforce with Einstein Studio.

Clean up

To delete all the resources created by the SageMaker project, on the project page, choose the Action menu and choose Delete.

To delete the resources (API Gateway and SageMaker endpoint) created by CodePipeline, navigate to the AWS CloudFormation console and delete the stack that was created.

Conclusion

In this post, we explained how you can build and train ML models in SageMaker Studio using SageMaker Data Wrangler to import and prepare data that is hosted on the Salesforce Data Cloud and use the newly launched Salesforce Data Cloud JDBC connector in SageMaker Data Wrangler and first-party Salesforce template in the SageMaker provided project template for Salesforce Data Cloud integration. The SageMaker project template for Salesforce enables you to deploy the model and create the endpoint and secure an API for a registered model. You then use the API to make predictions in Salesforce Einstein Studio for your business use cases.

Although we used the example of product recommendation to showcase the steps for implementing the end-to-end integration, you can use the SageMaker project template for Salesforce to create an endpoint and API for any SageMaker traditional model and LLM that is registered in the SageMaker Model Registry. We look forward to seeing what you build in SageMaker using data from Salesforce Data Cloud and empower your Salesforce applications using SageMaker hosted ML models!

This post is a continuation of the series regarding Salesforce Data Cloud and SageMaker integration. For a high-level overview and to learn more about the business impact you can make with this integration approach, refer to Part 1.

Additional resources

• Import data with SageMaker Data Wrangler
• Troubleshoot SageMaker Data Wrangler

About the authors

Daryl Martis is the Director of Product for Einstein Studio at Salesforce Data Cloud. He has over 10 years of experience in planning, building, launching, and managing world-class solutions for enterprise customers including AI/ML and cloud solutions. He has previously worked in the financial services industry in New York City. Follow him on https://www.linkedin.com/in/darylmartis.

Rachna Chadha is a Principal Solutions Architect AI/ML in Strategic Accounts at AWS. Rachna is an optimist who believes that ethical and responsible use of AI can improve society in the future and bring economic and social prosperity. In her spare time, Rachna likes spending time with her family, hiking, and listening to music.

Ife Stewart is a Principal Solutions Architect in the Strategic ISV segment at AWS. She has been engaged with Salesforce Data Cloud over the last 2 years to help build integrated customer experiences across Salesforce and AWS. Ife has over 10 years of experience in technology. She is an advocate for diversity and inclusion in the technology field.

Dharmendra Kumar Rai (DK Rai) is a Sr. Data Architect, Data Lake & AI/ML, serving strategic customers. He works closely with customers to understand how AWS can help them solve problems, especially in the AI/ML and analytics space. DK has many years of experience in building data-intensive solutions across a range of industry verticals, including high-tech, FinTech, insurance, and consumer-facing applications.

Marc Karp is an ML Architect with the SageMaker Service team. He focuses on helping customers design, deploy, and manage ML workloads at scale. In his spare time, he enjoys traveling and exploring new places.

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This post is co-authored by Daryl Martis, Director of Product, Salesforce Einstein AI.

We’re excited to announce Amazon SageMaker and Salesforce Data Cloud integration. With this capability, businesses can access their Salesforce data securely with a zero-copy approach using SageMaker and use SageMaker tools to build, train, and deploy AI models. The inference endpoints are connected with Data Cloud to drive predictions in real time. As a result, businesses can accelerate time to market while maintaining data integrity and security, and reduce the operational burden of moving data from one location to another.

Introducing Einstein Studio on Data Cloud

Data Cloud is a data platform that provides businesses with real-time updates of their customer data from any touch point. With Einstein Studio, a gateway to AI tools on the data platform, admins and data scientists can effortlessly create models with a few clicks or using code. Einstein Studio’s bring your own model (BYOM) experience provides the capability to connect custom or generative AI models from external platforms such as SageMaker to Data Cloud. Custom models can be trained using data from Salesforce Data Cloud accessed through the Amazon SageMaker Data Wrangler connector. Businesses can act on their predictions by seamlessly integrating custom models into Salesforce workflows, leading to improved efficiency, decision-making, and personalized experiences.

Benefits of the SageMaker and Data Cloud Einstein Studio integration

Here’s how using SageMaker with Einstein Studio in Salesforce Data Cloud can help businesses:

• It provides the ability to connect custom and generative AI models to Einstein Studio for various use cases, such as lead conversion, case classification, and sentiment analysis.
• It eliminates tedious, costly, and error-prone ETL (extract, transform, and load) jobs. The zero-copy approach to data reduces the overhead to manage data copies, reduces storage costs, and improves efficiencies.
• It provides access to highly curated, harmonized, and real-time data across Customer 360. This leads to expert models that deliver more intelligent predictions and business insights.
• It simplifies the consumption of results from business processes and drives value without latency. For example, you can use automated workflows that can adapt in an instant based on new data.
• It facilitates the operationalization of SageMaker models and inferences in Salesforce.

The following is an example of how to operationalize a SageMaker model using Salesforce Flow.

SageMaker integration

SageMaker is a fully managed service to prepare data and build, train, and deploy machine learning (ML) models for any use case with fully managed infrastructure, tools, and workflows.

To streamline the SageMaker and Salesforce Data Cloud integration, we are introducing two new capabilities in SageMaker:

• The SageMaker Data Wrangler Salesforce Data Cloud connector – With the newly launched SageMaker Data Wrangler Salesforce Data Cloud connector, admins can preconfigure connections to Salesforce to enable data analysts and data scientists to quickly access Salesforce data in real time and create features for ML. This will enable users to access Salesforce Data Cloud securely using OAuth. You can interactively visualize, analyze, and transform data using the power of Spark without writing any code using the low-code visual data preparation features of Salesforce Data Wrangler. You can also scale to process large datasets with SageMaker Processing jobs, train ML modes automatically using Amazon SageMaker Autopilot, and integrate with a SageMaker inference pipeline to deploy the same data flow to production with the inference endpoint to process data in real time or in batch for inference.

• The SageMaker Projects template for Salesforce – We launched a SageMaker Projects template for Salesforce that you can use to deploy endpoints for traditional and large language models (LLMs) and expose SageMaker endpoints as an API automatically. SageMaker Projects provides a straightforward way to set up and standardize the development environment for data scientists and ML engineers to build and deploy ML models on SageMaker.

Partner Quote

“The partnership between Salesforce and AWS Sagemaker will empower customers to leverage the power of AI (both, generative and non-generative models) across their Salesforce data sources, workflows and applications to deliver personalized experiences and power new content generation, summarization, and question-answer type experiences. By combining the best of both worlds, we are creating a new paradigm for data-driven innovation and customer success underpinned by AI.”

-Kaushal Kurapati, Salesforce Senior Vice President of Product, AI and Search

Solution overview

The BYOM integration solution provides customers with a native Salesforce Data Cloud connector in SageMaker Data Wrangler. The SageMaker Data Wrangler connector allows you to securely access Salesforce Data Cloud objects. Once users are authenticated, they can perform data exploration, preparation, and feature engineering tasks needed for model development and inference through the SageMaker Data Wrangler interactive visual interface. Data scientists can work within Amazon SageMaker Studio notebooks to develop custom models, which can be traditional or LLMs, and make them available for deployment by registering the model in the SageMaker Model Registry. When a model is approved for production in the registry, SageMaker Projects will automate the deployment of an invocation API that can be configured as a target in Salesforce Einstein Studio and integrated with Salesforce Customer 360 applications. The following diagram illustrates this architecture

Conclusion

In this post, we shared the SageMaker and Salesforce Einstein Studio BYOM integration, where you can use data in Salesforce Data Cloud to build and train traditional and LLMs in SageMaker. You can use SageMaker Data Wrangler to prepare data from Salesforce Data Cloud with zero copy. We also provided an automated solution to deploy the SageMaker endpoints as an API using a SageMaker Projects template for Salesforce.

AWS and Salesforce are excited to partner together to deliver this experience to our joint customers to help them drive business processes using the power of ML and artificial intelligence.

To learn more about the Salesforce BYOM integration, refer to Bring your own AI models with Einstein Studio. For a detailed implementation using product recommendations example use case, refer to Use the Amazon SageMaker and Salesforce Data Cloud integration to power your Salesforce Apps with AI/ML.

About the Authors

Daryl Martis is the Director of Product for Einstein Studio at Salesforce Data Cloud. He has over 10 years of experience in planning, building, launching, and managing world-class solutions for enterprise customers including AI/ML and cloud solutions. He has previously worked in the financial services industry in New York City.

Rachna Chadha is a Principal Solutions Architect AI/ML in Strategic Accounts at AWS. Rachna is an optimist who believes that the ethical and responsible use of AI can improve society in the future and bring economic and social prosperity. In her spare time, Rachna likes spending time with her family, hiking, and listening to music.

Ife Stewart is a Principal Solutions Architect in the Strategic ISV segment at AWS. She has been engaged with Salesforce Data Cloud over the last 2 years to help build integrated customer experiences across Salesforce and AWS. Ife has over 10 years of experience in technology. She is an advocate for diversity and inclusion in the technology field.

Maninder (Mani) Kaur is the AI/ML Specialist lead for Strategic ISVs at AWS. With her customer-first approach, Mani helps strategic customers shape their AI/ML strategy, fuel innovation, and accelerate their AI/ML journey. Mani is a firm believer of ethical and responsible AI, and strives to ensure that her customers’ AI solutions align with these principles.

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Data classification, extraction, and analysis can be challenging for organizations that deal with volumes of documents. Traditional document processing solutions are manual, expensive, error prone, and difficult to scale. AWS intelligent document processing (IDP), with AI services such as Amazon Textract, allows you to take advantage of industry-leading machine learning (ML) technology to quickly and accurately process data from any scanned document or image. Generative artificial intelligence (generative AI) complements Amazon Textract to further automate document processing workflows. Features such as normalizing key fields and summarizing input data support faster cycles for managing document process workflows, while reducing the potential for errors.

Generative AI is driven by large ML models called foundation models (FMs). FMs are transforming the way you can solve traditionally complex document processing workloads. In addition to existing capabilities, businesses need to summarize specific categories of information, including debit and credit data from documents such as financial reports and bank statements. FMs make it easier to generate such insights from the extracted data. To optimize time spent in human review and to improve employee productivity, mistakes such as missing digits in phone numbers, missing documents, or addresses without street numbers can be flagged in an automated way. In the current scenario, you need to dedicate resources to accomplish such tasks using human review and complex scripts. This approach is tedious and expensive. FMs can help complete these tasks faster, with fewer resources, and transform varying input formats into a standard template that can be processed further. At AWS, we offer services such as Amazon Bedrock, the easiest way to build and scale generative AI applications with FMs. Amazon Bedrock is a fully managed service that makes FMs from leading AI startups and Amazon available through an API, so you can find the model that best suits your requirements. We also offer Amazon SageMaker JumpStart, which allows ML practitioners to choose from a broad selection of open-source FMs. ML practitioners can deploy FMs to dedicated Amazon SageMaker instances from a network isolated environment and customize models using SageMaker for model training and deployment.

Ricoh offers workplace solutions and digital transformation services designed to help customers manage and optimize information flow across their businesses. Ashok Shenoy, VP of Portfolio Solution Development, says, “We are adding generative AI to our IDP solutions to help our customers get their work done faster and more accurately by utilizing new capabilities such as Q&A, summarization, and standardized outputs. AWS allows us to take advantage of generative AI while keeping each of our customers’ data separate and secure.”

In this post, we share how to enhance your IDP solution on AWS with generative AI.

Improving the IDP pipeline

In this section, we review how the traditional IDP pipeline can be augmented by FMs and walk through an example use case using Amazon Textract with FMs.

AWS IDP is comprised of three stages: classification, extraction, and enrichment. For more details about each stage, refer to Intelligent document processing with AWS AI services: Part 1 and Part 2. In the classification stage, FMs can now classify documents without any additional training. This means that documents can be categorized even if the model hasn’t seen similar examples before. FMs in the extraction stage normalize date fields and verify addresses and phone numbers, while ensuring consistent formatting. FMs in the enrichment stage allow inference, logical reasoning, and summarization. When you use FMs in each IDP stage, your workflow will be more streamlined and performance will improve. The following diagram illustrates the IDP pipeline with generative AI.

Extraction stage of the IDP pipeline

When FMs can’t directly process documents in their native formats (such as PDFs, img, jpeg, and tiff) as an input, a mechanism to convert documents to text is needed. To extract the text from the document before sending it to the FMs, you can use Amazon Textract. With Amazon Textract, you can extract lines and words and pass them to downstream FMs. The following architecture uses Amazon Textract for accurate text extraction from any type of document before sending it to FMs for further processing.

Typically, documents are comprised of structured and semi-structured information. Amazon Textract can be used to extract raw text and data from tables and forms. The relationship between the data in tables and forms plays a vital role in automating business processes. Certain types of information may not be processed by FMs. As a result, we can choose to either store this information in a downstream store or send it to FMs. The following figure is an example of how Amazon Textract can extract structured and semi-structured information from a document, in addition to lines of text that need to be processed by FMs.

Using AWS serverless services to summarize with FMs

The IDP pipeline we illustrated earlier can be seamlessly automated using AWS serverless services. Highly unstructured documents are common in big enterprises. These documents can span from Securities and Exchange Commission (SEC) documents in the banking industry to coverage documents in the health insurance industry. With the evolution of generative AI at AWS, people in these industries are looking for ways to get a summary from those documents in an automated and cost-effective manner. Serverless services help provide the mechanism to build a solution for IDP quickly. Services such as AWS Lambda, AWS Step Functions, and Amazon EventBridge can help build the document processing pipeline with integration of FMs, as shown in the following diagram.

The sample application used in the preceding architecture is driven by events. An event is defined as a change in state that has recently occurred. For example, when an object gets uploaded to an Amazon Simple Storage Service (Amazon S3) bucket, Amazon S3 emits an Object Created event. This event notification from Amazon S3 can trigger a Lambda function or a Step Functions workflow. This type of architecture is termed as an event-driven architecture. In this post, our sample application uses an event-driven architecture to process a sample medical discharge document and summarize the details of the document. The flow works as follows:

1. When a document is uploaded to an S3 bucket, Amazon S3 triggers an Object Created event.
2. The EventBridge default event bus propagates the event to Step Functions based on an EventBridge rule.
3. The state machine workflow processes the document, beginning with Amazon Textract.
4. A Lambda function transforms the analyzed data for the next step.
5. The state machine invokes a SageMaker endpoint, which hosts the FM using direct AWS SDK integration.
6. A summary S3 destination bucket receives the summary response gathered from the FM.

We used the sample application with a flan-t5 Hugging face model to summarize the following sample patient discharge summary using the Step Functions workflow.

The Step Functions workflow uses AWS SDK integration to call the Amazon Textract AnalyzeDocument and SageMaker runtime InvokeEndpoint APIs, as shown in the following figure.

This workflow results in a summary JSON object that is stored in a destination bucket. The JSON object looks as follows:

{
  "summary": [
    "John Doe is a 35-year old male who has been experiencing stomach problems for two months. He has been taking antibiotics for the last two weeks, but has not been able to eat much. He has been experiencing a lot of abdominal pain, bloating, and fatigue. He has also noticed a change in his stool color, which is now darker. He has been taking antacids for the last two weeks, but they no longer help. He has been experiencing a lot of fatigue, and has been unable to work for the last two weeks. He has also been experiencing a lot of abdominal pain, bloating, and fatigue. He has been taking antacids for the last two weeks, but they no longer help. He has been experiencing a lot of abdominal pain, bloating, and fatigue. He has been taking antacids for the last two weeks, but they no longer help. He has been experiencing a lot of abdominal pain, bloating, and fatigue. He has been taking antacids for the last two weeks, but they no longer help. He has been experiencing a lot of abdominal pain, bloating, and fatigue. He has been taking antacids for the last two weeks, but they no longer help."
  ],
  "forms": [
    {
      "key": "Ph: ",
      "value": "(888)-(999)-(0000) "
    },
    {
      "key": "Fax: ",
      "value": "(888)-(999)-(1111) "
    },
    {
      "key": "Patient Name: ",
      "value": "John Doe "
    },
    {
      "key": "Patient ID: ",
      "value": "NARH-36640 "
    },
    {
      "key": "Gender: ",
      "value": "Male "
    },
    {
      "key": "Attending Physician: ",
      "value": "Mateo Jackson, PhD "
    },
    {
      "key": "Admit Date: ",
      "value": "07-Sep-2020 "
    },
    {
      "key": "Discharge Date: ",
      "value": "08-Sep-2020 "
    },
    {
      "key": "Discharge Disposition: ",
      "value": "Home with Support Services "
    },
    {
      "key": "Pre-existing / Developed Conditions Impacting Hospital Stay: ",
      "value": "35 yo M c/o stomach problems since 2 months. Patient reports epigastric abdominal pain non- radiating. Pain is described as gnawing and burning, intermittent lasting 1-2 hours, and gotten progressively worse. Antacids used to alleviate pain but not anymore; nothing exacerbates pain. Pain unrelated to daytime or to meals. Patient denies constipation or diarrhea. Patient denies blood in stool but have noticed them darker. Patient also reports nausea. Denies recent illness or fever. He also reports fatigue for 2 weeks and bloating after eating. ROS: Negative except for above findings Meds: Motrin once/week. Tums previously. PMHx: Back pain and muscle spasms. No Hx of surgery. NKDA. FHx: Uncle has a bleeding ulcer. Social Hx: Smokes since 15 yo, 1/2-1 PPD. No recent EtOH use. Denies illicit drug use. Works on high elevation construction. Fast food diet. Exercises 3-4 times/week but stopped 2 weeks ago. "
    },
    {
      "key": "Summary: ",
      "value": "some activity restrictions suggested, full course of antibiotics, check back with physican in case of relapse, strict diet "
    }
  ]
 }

Generating these summaries using IDP with serverless implementation at scale helps organizations get meaningful, concise, and presentable data in a cost-effective way. Step Functions doesn’t limit the method of processing documents to one document at a time. Its distributed map feature can summarize large numbers of documents on a schedule.

The sample application uses a flan-t5 Hugging face model; however, you can use an FM endpoint of your choice. Training and running the model is out of scope of the sample application. Follow the instructions in the GitHub repository to deploy a sample application. The preceding architecture is a guidance on how you can orchestrate an IDP workflow using Step Functions. Refer to the IDP Generative AI workshop for detailed instructions on how to build an application with AWS AI services and FMs.

Set up the solution

Follow the steps in the README file to set the solution architecture (except for the SageMaker endpoints). After you have your own SageMaker endpoint available, you can pass the endpoint name as a parameter to the template.

Clean up

To save costs, delete the resources you deployed as part of the tutorial:

1. Follow the steps in the cleanup section of the README file.
2. Delete any content from your S3 bucket and then delete the bucket through the Amazon S3 console.
3. Delete any SageMaker endpoints you may have created through the SageMaker console.

Conclusion

Generative AI is changing how you can process documents with IDP to derive insights. AWS AI services such as Amazon Textract along with AWS FMs can help accurately process any type of documents. For more information on working with generative AI on AWS, refer to Announcing New Tools for Building with Generative AI on AWS.

About the Authors

Sonali Sahu is leading intelligent document processing with the AI/ML services team in AWS. She is an author, thought leader, and passionate technologist. Her core area of focus is AI and ML, and she frequently speaks at AI and ML conferences and meetups around the world. She has both breadth and depth of experience in technology and the technology industry, with industry expertise in healthcare, the financial sector, and insurance.

Ashish Lal is a Senior Product Marketing Manager who leads product marketing for AI services at AWS. He has 9 years of marketing experience and has led the product marketing effort for Intelligent document processing. He got his Master’s in Business Administration at the University of Washington.

Mrunal Daftari is an Enterprise Senior Solutions Architect at Amazon Web Services. He is based in Boston, MA. He is a cloud enthusiast and very passionate about finding solutions for customers that are simple and address their business outcomes. He loves working with cloud technologies, providing simple, scalable solutions that drive positive business outcomes, cloud adoption strategy, and design innovative solutions and drive operational excellence.

Dhiraj Mahapatro is a Principal Serverless Specialist Solutions Architect at AWS. He specializes in helping enterprise financial services adopt serverless and event-driven architectures to modernize their applications and accelerate their pace of innovation. Recently, he has been working on bringing container workloads and practical usage of generative AI closer to serverless and EDA for financial services industry customers.

Jacob Hauskens is a Principal AI Specialist with over 15 years of strategic business development and partnerships experience. For the past 7 years, he has led the creation and implementation of go-to-market strategies for new AI-powered B2B services. Recently, he has been helping ISVs grow their revenue by adding generative AI to intelligent document processing workflows.

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As machine learning (ML) becomes increasingly prevalent in a wide range of industries, organizations are finding the need to train and serve large numbers of ML models to meet the diverse needs of their customers. For software as a service (SaaS) providers in particular, the ability to train and serve thousands of models efficiently and cost-effectively is crucial for staying competitive in a rapidly evolving market.

Training and serving thousands of models requires a robust and scalable infrastructure, which is where Amazon SageMaker can help. SageMaker is a fully managed platform that enables developers and data scientists to build, train, and deploy ML models quickly, while also offering the cost-saving benefits of using the AWS Cloud infrastructure.

In this post, we explore how you can use SageMaker features, including Amazon SageMaker Processing, SageMaker training jobs, and SageMaker multi-model endpoints (MMEs), to train and serve thousands of models in a cost-effective way. To get started with the described solution, you can refer to the accompanying notebook on GitHub.

Use case: Energy forecasting

For this post, we assume the role of an ISV company that helps their customers become more sustainable by tracking their energy consumption and providing forecasts. Our company has 1,000 customers who want to better understand their energy usage and make informed decisions about how to reduce their environmental impact. To do this, we use a synthetic dataset and train an ML model based on Prophet for each customer to make energy consumption forecasts. With SageMaker, we can efficiently train and serve these 1,000 models, providing our customers with accurate and actionable insights into their energy usage.

There are three features in the generated dataset:

• customer_id – This is an integer identifier for each customer, ranging from 0–999.
• timestamp – This is a date/time value that indicates the time at which the energy consumption was measured. The timestamps are randomly generated between the start and end dates specified in the code.
• consumption – This is a float value that indicates the energy consumption, measured in some arbitrary unit. The consumption values are randomly generated between 0–1,000 with sinusoidal seasonality.

Solution overview

To efficiently train and serve thousands of ML models, we can use the following SageMaker features:

• SageMaker Processing – SageMaker Processing is a fully managed data preparation service that enables you to perform data processing and model evaluation tasks on your input data. You can use SageMaker Processing to transform raw data into the format needed for training and inference, as well as to run batch and online evaluations of your models.
• SageMaker training jobs – You can use SageMaker training jobs to train models on a variety of algorithms and input data types, and specify the compute resources needed for training.
• SageMaker MMEs – Multi-model endpoints enable you to host multiple models on a single endpoint, which makes it easy to serve predictions from multiple models using a single API. SageMaker MMEs can save time and resources by reducing the number of endpoints needed to serve predictions from multiple models. MMEs support hosting of both CPU- and GPU-backed models. Note that in our scenario, we use 1,000 models, but this is not a limitation of the service itself.

The following diagram illustrates the solution architecture.

The workflow includes the following steps:

1. We use SageMaker Processing to preprocess data and create a single CSV file per customer and store it in Amazon Simple Storage Service (Amazon S3).
2. The SageMaker training job is configured to read the output of the SageMaker Processing job and distribute it in a round-robin fashion to the training instances. Note that this can also be achieved with Amazon SageMaker Pipelines.
3. The model artifacts are stored in Amazon S3 by the training job, and are served directly from the SageMaker MME.

Scale training to thousands of models

Scaling the training of thousands of models is possible via the distribution parameter of the TrainingInput class in the SageMaker Python SDK, which allows you to specify how data is distributed across multiple training instances for a training job. There are three options for the distribution parameter: FullyReplicated, ShardedByS3Key, and ShardedByRecord. The ShardedByS3Key option means that the training data is sharded by S3 object key, with each training instance receiving a unique subset of the data, avoiding duplication. After the data is copied by SageMaker to the training containers, we can read the folder and files structure to train a unique model per customer file. The following is an example code snippet:

# Assume that the training data is in an S3 bucket already, pass the parent folder
s3_input_train = sagemaker.inputs.TrainingInput(
    s3_data='s3://my-bucket/customer_data',
    distribution='ShardedByS3Key'
)

# Create a SageMaker estimator and set the training input
estimator = sagemaker.estimator.Estimator(...)
estimator.fit(inputs=s3_input_train)

Every SageMaker training job stores the model saved in the /opt/ml/model folder of the training container before archiving it in a model.tar.gz file, and then uploads it to Amazon S3 upon training job completion. Power users can also automate this process with SageMaker Pipelines. When storing multiple models via the same training job, SageMaker creates a single model.tar.gz file containing all the trained models. This would then mean that, in order to serve the model, we would need to unpack the archive first. To avoid this, we use checkpoints to save the state of individual models. SageMaker provides the functionality to copy checkpoints created during the training job to Amazon S3. Here, the checkpoints need to be saved in a pre-specified location, with the default being /opt/ml/checkpoints. These checkpoints can be used to resume training at a later moment or as a model to deploy on an endpoint. For a high-level summary of how the SageMaker training platform manages storage paths for training datasets, model artifacts, checkpoints, and outputs between AWS Cloud storage and training jobs in SageMaker, refer to Amazon SageMaker Training Storage Folders for Training Datasets, Checkpoints, Model Artifacts, and Outputs.

The following code uses a fictitious model.save() function inside the train.py script containing the training logic:

import tarfile
import boto3
import os

[ ... argument parsing ... ]

for customer in os.list_dir(args.input_path):
    
    # Read data locally within the Training job
    df = pd.read_csv(os.path.join(args.input_path, customer, 'data.csv'))
    
    # Define and train the model
    model = MyModel()
     model.fit(df)
            
    # Save model to output directory
    with open(os.path.join(output_dir, 'model.json'), 'w') as fout:
        fout.write(model_to_json(model))
    
    # Create the model.tar.gz archive containing the model and the training script
    with tarfile.open(os.path.join(output_dir, '{customer}.tar.gz'), "w:gz") as tar:
        tar.add(os.path.join(output_dir, 'model.json'), "model.json")
        tar.add(os.path.join(args.code_dir, "training.py"), "training.py")

Scale inference to thousands of models with SageMaker MMEs

SageMaker MMEs allow you to serve multiple models at the same time by creating an endpoint configuration that includes a list of all the models to serve, and then creating an endpoint using that endpoint configuration. There is no need to re-deploy the endpoint every time you add a new model because the endpoint will automatically serve all models stored in the specified S3 paths. This is achieved with Multi Model Server (MMS), an open-source framework for serving ML models that can be installed in containers to provide the front end that fulfills the requirements for the new MME container APIs. In addition, you can use other model servers including TorchServe and Triton. MMS can be installed in your custom container via the SageMaker Inference Toolkit. To learn more about how to configure your Dockerfile to include MMS and use it to serve your models, refer to Build Your Own Container for SageMaker Multi-Model Endpoints.

The following code snippet shows how to create an MME using the SageMaker Python SDK:

from sagemaker.multidatamodel import MultiDataModel

# Create the MultiDataModel definition
multimodel = MultiDataModel(
    name='customer-models',
    model_data_prefix=f's3://{bucket}/scaling-thousand-models/models',
    model=your_model,
)

# Deploy on a real-time endpoint
predictor = multimodel.deploy(
    initial_instance_count=1,
    instance_type='ml.c5.xlarge',
)

When the MME is live, we can invoke it to generate predictions. Invocations can be done in any AWS SDK as well as with the SageMaker Python SDK, as shown in the following code snippet:

predictor.predict(
    data='{"period": 7}',             # the payload, in this case JSON
    target_model='{customer}.tar.gz'  # the name of the target model
)

When calling a model, the model is initially loaded from Amazon S3 on the instance, which can result in a cold start when calling a new model. Frequently used models are cached in memory and on disk to provide low-latency inference.

Conclusion

SageMaker is a powerful and cost-effective platform for training and serving thousands of ML models. Its features, including SageMaker Processing, training jobs, and MMEs, enable organizations to efficiently train and serve thousands of models at scale, while also benefiting from the cost-saving advantages of using the AWS Cloud infrastructure. To learn more about how to use SageMaker for training and serving thousands of models, refer to Process data, Train a Model with Amazon SageMaker and Host multiple models in one container behind one endpoint.

About the Authors

Davide Gallitelli is a 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.

Maurits de Groot is a Solutions Architect at Amazon Web Services, based out of Amsterdam. He likes to work on machine learning-related topics and has a predilection for startups. In his spare time, he enjoys skiing and playing squash.

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Amazon SageMaker Canvas is a visual interface that enables business analysts to generate accurate machine learning (ML) predictions on their own, without requiring any ML experience or having to write a single line of code. SageMaker Canvas’s intuitive user interface lets business analysts browse and access disparate data sources in the cloud or on premises, prepare and explore the data, build and train ML models, and generate accurate predictions within a single workspace.

SageMaker Canvas allows analysts to use different data workloads to achieve the desired business outcomes with high accuracy and performance. The compute, storage, and memory requirements to generate accurate predictions are abstracted from the end-user, enabling them to focus on the business problem to be solved. Earlier this year, we announced performance optimizations based on customer feedback to deliver faster and more accurate model training times with SageMaker Canvas.

In this post, we show how SageMaker Canvas can now process data, train models, and generate predictions with increased speed and efficiency for different dataset sizes.

Prerequisites

If you would like to follow along, complete the following prerequisites:

1. Have an AWS account.
2. Set up SageMaker Canvas. For instructions, refer to Prerequisites for setting up Amazon SageMaker Canvas.
3. Download the following two datasets to your local computer. The first is the NYC Yellow Taxi Trip dataset; the second is the eCommerce behavior data about retails events related to products and users.

Both datasets come under the Attribution 4.0 International (CC BY 4.0) license and are free to share and adapt.

Data processing improvements

With underlying performance optimizations, the time to import data into SageMaker Canvas has improved by over 70%. You can now import datasets of up to 2 GB in approximately 50 seconds and up to 5 GB in approximately 65 seconds.

After importing data, business analysts typically validate the data to ensure there are no issues found within the dataset. Example validation checks can be ensuring columns contain the correct data type, seeing if the value ranges are in line with expectations, making sure there is uniqueness in values where applicable, and others.

Data validation is now faster. In our tests, all validations took 50 seconds for the taxi dataset exceeding 5 GB in size, a 10-times improvement in speed.

Model training improvements

The performance optimizations related to ML model training in SageMaker Canvas now enable you to train models without running into potential out-of-memory requests failures.

The following screenshot shows the results of a successful build run using a large dataset the impact of the total_amount feature on the target variable.

Inference improvements

Finally, SageMaker Canvas inference improvements achieved a 3.5 times reduction memory consumption in case of larger datasets in our internal testing.

Conclusion

In this post, we saw various improvements with SageMaker Canvas in importing, validation, training, and inference. We saw an increased in its ability to import large datasets by 70%. We saw a 10 times improvement in data validation, and a 3.5 times reduction in memory consumption. These improvements allow you to better work with large datasets and reduce time when building ML models with SageMaker Canvas.

We encourage you to experience the improvements yourself. We welcome your feedback as we continuously work on performance optimizations to improve the user experience.

About the authors

Peter Chung is a Solutions Architect for AWS, and is passionate about helping customers uncover insights from their data. He has been building solutions to help organizations make data-driven decisions in both the public and private sectors. He holds all AWS certifications as well as two GCP certifications. He enjoys coffee, cooking, staying active, and spending time with his family.

Tim Song is a Software Development Engineer at AWS SageMaker, with 10+ years of experience as software developer, consultant and tech leader he has demonstrated ability to deliver scalable and reliable products and solve complex problems. In his spare time, he enjoys the nature, outdoor running, hiking and etc.

Hariharan Suresh is a Senior Solutions Architect at AWS. He is passionate about databases, machine learning, and designing innovative solutions. Prior to joining AWS, Hariharan was a product architect, core banking implementation specialist, and developer, and worked with BFSI organizations for over 11 years. Outside of technology, he enjoys paragliding and cycling.

Maia Haile is a Solutions Architect at Amazon Web Services based in the Washington, D.C. area. In that role, she helps public sector customers achieve their mission objectives with well architected solutions on AWS. She has 5 years of experience spanning from nonprofit healthcare, Media and Entertainment, and retail. Her passion is leveraging intelligence (AI) and machine learning (ML) to help Public Sector customers achieve their business and technical goals.

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Computer vision (CV) is one of the most common applications of machine learning (ML) and deep learning. Use cases range from self-driving cars, content moderation on social media platforms, cancer detection, and automated defect detection. Amazon Rekognition is a fully managed service that can perform CV tasks like object detection, video segment detection, content moderation, and more to extract insights from data without the need of any prior ML experience. In some cases, a more custom solution might be needed along with the service to solve a very specific problem.

In this post, we address areas where CV can be applied to use cases where the pose of objects, their position, and orientation is important. One such use case would be customer-facing mobile applications where an image upload is required. It might be for compliance reasons or to provide a consistent user experience and improve engagement. For example, on online shopping platforms, the angle at which products are shown in images has an effect on the rate of buying this product. One such case is to detect the position of a car. We demonstrate how you can combine well-known ML solutions with postprocessing to address this problem on the AWS Cloud.

We use deep learning models to solve this problem. Training ML algorithms for pose estimation requires a lot of expertise and custom training data. Both requirements are hard and costly to obtain. Therefore, we present two options: one that doesn’t require any ML expertise and uses Amazon Rekognition, and another that uses Amazon SageMaker to train and deploy a custom ML model. In the first option, we use Amazon Rekognition to detect the wheels of the car. We then infer the car orientation from the wheel positions using a rule-based system. In the second option, we detect the wheels and other car parts using the Detectron model. These are again used to infer the car position with rule-based code. The second option requires ML experience but is also more customizable. It can be used for further postprocessing on the image, for example, to crop out the whole car. Both of the options can be trained on publicly available datasets. Finally, we show how you can integrate this car pose detection solution into your existing web application using services like Amazon API Gateway and AWS Amplify.

Solution overview

The following diagram illustrates the solution architecture.

The solution consists of a mock web application in Amplify where a user can upload an image and invoke either the Amazon Rekognition model or the custom Detectron model to detect the position of the car. For each option, we host an AWS Lambda function behind an API Gateway that is exposed to our mock application. We configured our Lambda function to run with either the Detectron model trained in SageMaker or Amazon Rekognition.

Prerequisites

For this walkthrough, you should have the following prerequisites:

• An AWS account.
• An AWS Identity and Access Management (IAM) user with the permissions to deploy and provision the infrastructure, for example, PowerUserAccess (note that permissions would need to be restricted further for a production-ready application and depend on possible integrations with other services).
• Docker in your development environment (local machine or a SageMaker notebook instance where you are deploying the solution from).
• The AWS Cloud Development Kit (AWS CDK) installed. It can be installed using npm as explained in our GitHub repository.

Create a serverless app using Amazon Rekognition

Our first option demonstrates how you can detect car orientations in images using Amazon Rekognition. The idea is to use Amazon Rekognition to detect the location of the car and its wheels and then do postprocessing to derive the orientation of the car from this information. The whole solution is deployed using Lambda as shown in the Github repository. This folder contains two main files: a Dockerfile that defines the Docker image that will run in our Lambda function, and the app.py file, which will be the main entry point of the Lambda function:

def lambda_handler(event, context):
    body_bytes = json.loads(event["body"])["image"].split(",")[-1]
    body_bytes = base64.b64decode(body_bytes)

    rek = boto3.client('rekognition')
    response = rek.detect_labels(Image={'Bytes': body_bytes}, MinConfidence=80)
    
    angle, img = label_image(img_string=body_bytes, response=response)

    buffered = BytesIO()
    img.save(buffered, format="JPEG")
    img_str = "data:image/jpeg;base64," + base64.b64encode(buffered.getvalue()).decode('utf-8')

The Lambda function expects an event that contains a header and body, where the body should be the image needed to be labeled as base64 decoded object. Given the image, the Amazon Rekognition detect_labels function is invoked from the Lambda function using Boto3. The function returns one or more labels for each object in the image and bounding box details for all of the detected object labels as part of the response, along with other information like confidence of the assigned label, the ancestor labels of the detected label, possible aliases for the label, and the categories the detected label belongs to. Based on the labels returned by Amazon Rekognition, we run the function label_image, which calculates the car angle from the detected wheels as follows:

n_wheels = len(wheel_instances)

wheel_centers = [np.array(_extract_bb_coords(wheel, img)).mean(axis=0)
for wheel in wheel_instances]

wheel_center_comb = list(combinations(wheel_centers, 2))
vecs = [(k, pair[0] - pair[1]) for k,pair in enumerate(wheel_center_comb)]
vecs = sorted(vecs, key = lambda vec: np.linalg.norm(vec[1]))

vec_rel = vecs[1] if n_wheels == 3 else vecs[0]
angle = math.degrees(math.atan(vec_rel[1][1]/vec_rel[1][0]))

wheel_centers_rel = [tuple(wheel.tolist()) for wheel in
wheel_center_comb[vec_rel[0]]]

Note that the application requires that only one car is present in the image and returns an error if that’s not the case. However, the postprocessing can be adapted to provide more granular orientation descriptions, cover several cars, or calculate the orientation of more complex objects.

Improve wheel detection

To further improve the accuracy of the wheel detection, you can use Amazon Rekognition Custom Labels. Similar to fine-tuning using SageMaker to train and deploy a custom ML model, you can bring your own labeled data so that Amazon Rekognition can produce a custom image analysis model for you in just a few hours. With Rekognition Custom Labels, you only need a small set of training images that are specific to your use case, in this case car images with specific angles, because it uses the existing capabilities in Amazon Rekognition of being trained on tens of millions of images across many categories. Rekognition Custom Labels can be integrated with only a few clicks and small adaptations to the Lambda function we use for the standard Amazon Rekognition solution.

Train a model using a SageMaker training job

In our second option, we train a custom deep learning model on SageMaker. We use the Detectron2 framework for the segmentation of car parts. These segments are then used to infer the position of the car.

The Detectron2 framework is a library that provides state-of-the-art detection and segmentation algorithms. Detectron provides a variety of Mask R-CNN models that were trained on the famous COCO (Common objects in Context) dataset. To build our car objects detection model, we use transfer learning to fine-tune a pretrained Mask R-CNN model on the car parts segmentation dataset. This dataset allows us to train a model that can detect wheels but also other car parts. This additional information can be further used in the car angle computations relative to the image.

The dataset contains annotated data of car parts to be used for object detection and semantic segmentation tasks: approximately 500 images of sedans, pickups, and sports utility vehicles (SUVs), taken in multiple views (front, back, and side views). Each image is annotated by 18 instance masks and bounding boxes representing the different parts of a car like wheels, mirrors, lights, and front and back glass. We modified the base annotations of the wheels such that each wheel is considered an individual object instead of considering all the available wheels in the image as one object.

We use Amazon Simple Storage Service (Amazon S3) to store the dataset used for training the Detectron model along with the trained model artifacts. Moreover, the Docker container that runs in the Lambda function is stored in Amazon Elastic Container Registry (Amazon ECR). The Docker container in the Lambda function is needed to include the required libraries and dependencies for running the code. We could alternatively use Lambda layers, but it’s limited to an unzipped deployment packaged size quota of 250 MB and a maximum of five layers can be added to a Lambda function.

Our solution is built on SageMaker: we extend prebuilt SageMaker Docker containers for PyTorch to run our custom PyTorch training code. Next, we use the SageMaker Python SDK to wrap the training image into a SageMaker PyTorch estimator, as shown in the following code snippets:

d2_estimator = Estimator(
        image_uri=training_image_uri,
        role=role,
        sagemaker_session=sm_session,
        instance_count=1,
        instance_type=training_instance,
        output_path=f"s3://{session_bucket}/{prefix_model}",
        base_job_name=f"detectron2")

d2_estimator.fit({
            "training": training_channel,
            "validation": validation_channel,
        },
        wait=True)

Finally, we start the training job by calling the fit() function on the created PyTorch estimator. When the training is finished, the trained model artifact is stored in the session bucket in Amazon S3 to be used for the inference pipeline.

Deploy the model using SageMaker and inference pipelines

We also use SageMaker to host the inference endpoint that runs our custom Detectron model. The full infrastructure used to deploy our solution is provisioned using the AWS CDK. We can host our custom model through a SageMaker real-time endpoint by calling deploy on the PyTorch estimator. This is the second time we extend a prebuilt SageMaker PyTorch container to include PyTorch Detectron. We use it to run the inference script and host our trained PyTorch model as follows:

model = PyTorchModel(
        name="d2-sku110k-model",
        model_data=d2_estimator.model_data,
        role=role,
        sagemaker_session=sm_session,
        entry_point="predict.py",
        source_dir="src",
        image_uri=serve_image_uri,
        framework_version="1.6.0")

    predictor = model.deploy(
        initial_instance_count=1,
        instance_type="ml.g4dn.xlarge",
        endpoint_name="detectron-endpoint",
        serializer=sagemaker.serializers.JSONSerializer(),
        deserializer=sagemaker.deserializers.JSONDeserializer(),
        wait=True)

Note that we used an ml.g4dn.xlarge GPU for deployment because it’s the smallest GPU available and sufficient for this demo. Two components need to be configured in our inference script: model loading and model serving. The function model_fn() is used to load the trained model that is part of the hosted Docker container and can also be found in Amazon S3 and return a model object that can be used for model serving as follows:

def model_fn(model_dir: str) -> DefaultPredictor:
  
    for p_file in Path(model_dir).iterdir():
        if p_file.suffix == ".pth":
            path_model = p_file
        
    cfg = get_cfg()
    cfg.MODEL.WEIGHTS = str(path_model)

    return DefaultPredictor(cfg)

The function predict_fn() performs the prediction and returns the result. Besides using our trained model, we use a pretrained version of the Mask R-CNN model trained on the COCO dataset to extract the main car in the image. This is an extra postprocessing step to deal with images where more than one car exists. See the following code:

def predict_fn(input_img: np.ndarray, predictor: DefaultPredictor) -> Mapping:
    
    pretrained_predictor = _get_pretraind_model()
    car_mask = get_main_car_mask(pretrained_predictor, input_img)
    outputs = predictor(input_img)
    fmt_out = {
        "image_height": input_object.shape[0],
        "image_width": input_object.shape[1],
        "pred_boxes": outputs["instances"].pred_boxes.tensor.tolist(),
        "scores": outputs["instances"].scores.tolist(),
        "pred_classes": outputs["instances"].pred_classes.tolist(),
        "car_mask": car_mask.tolist()
    }
    return fmt_out

Similar to the Amazon Rekognition solution, the bounding boxes predicted for the wheel class are filtered from the detection outputs and supplied to the postprocessing module to assess the car position relative to the output.

Finally, we also improved the postprocessing for the Detectron solution. It also uses the segments of different car parts to infer the solution. For example, whenever a front bumper is detected, but no back bumper, it is assumed that we have a front view of the car and the corresponding angle is calculated.

Connect your solution to the web application

The steps to connect the model endpoints to Amplify are as follows:

• Clone the application repository that the AWS CDK stack created, named car-angle-detection-website-repo. Make sure you are looking for it in the Region you used for deployment.
• Copy the API Gateway endpoints for each of the deployed Lambda functions into the index.html file in the preceding repository (there are placeholders where the endpoint needs to be placed). The following code is an example of what this section of the .html file looks like:

<td align="center" colspan="2">
<select id="endpoint">
<option value="https://ey82aaj8ch.execute-api.eu-central-1.amazonaws.com/prod/">
                Amazon Rekognition</option>
<option value="https://nhq6q88xjg.execute-api.eu-central-1.amazonaws.com/prod/">
                Amazon SageMaker Detectron</option>
</select>
<input class="btn" type="file" id="ImageBrowse" />
<input class="btn btn-primary" type="submit" value="Upload">
</td>

• Save the HTML file and push the code change to the remote main branch.

This will update the HTML file in the deployment. The application is now ready to use.

• Navigate to the Amplify console and locate the project you created.

The application URL will be visible after the deployment is complete.

• Navigate to the URL and have fun with the UI.

Conclusion

Congratulations! We have deployed a complete serverless architecture in which we used Amazon Rekognition, but also gave an option for your own custom model, with this example available on GitHub. If you don’t have ML expertise in your team or enough custom data to train a model, you could select the option that uses Amazon Rekognition. If you want more control over your model, would like to customize it further, and have enough data, you can choose the SageMaker solution. If you have a team of data scientists, they might also want to enhance the models further and pick a more custom and flexible option. You can put the Lambda function and the API Gateway behind your web application using either of the two options. You can also use this approach for a different use case for which you might want to adapt the code.

The advantage of this serverless architecture is that the building blocks are completely exchangeable. The opportunities are almost limitless. So, get started today!

As always, AWS welcomes feedback. Please submit any comments or questions.

About the Authors

Michael Wallner is a Senior Consultant Data & AI with AWS Professional Services and is passionate about enabling customers on their journey to become data-driven and AWSome in the AWS cloud. On top, he likes thinking big with customers to innovate and invent new ideas for them.

Aamna Najmi is a Data Scientist with AWS Professional Services. She is passionate about helping customers innovate with Big Data and Artificial Intelligence technologies to tap business value and insights from data. She has experience in working on data platform and AI/ML projects in the healthcare and life sciences vertical. In her spare time, she enjoys gardening and traveling to new places.

David Sauerwein is a Senior Data Scientist at AWS Professional Services, where he enables customers on their AI/ML journey on the AWS cloud. David focuses on digital twins, forecasting and quantum computation. He has a PhD in theoretical physics from the University of Innsbruck, Austria. He was also a doctoral and post-doctoral researcher at the Max-Planck-Institute for Quantum Optics in Germany. In his free time he loves to read, ski and spend time with his family.

Srikrishna Chaitanya Konduru is a Senior Data Scientist with AWS Professional services. He supports customers in prototyping and operationalising their ML applications on AWS. Srikrishna focuses on computer vision and NLP. He also leads ML platform design and use case identification initiatives for customers across diverse industry verticals. Srikrishna has an M.Sc in Biomedical Engineering from RWTH Aachen university, Germany, with a focus on Medical Imaging.

Ahmed Mansour is a Data Scientist at AWS Professional Services. He provide technical support for customers through their AI/ML journey on the AWS cloud. Ahmed focuses on applications of NLP to the protein domain along with RL. He has a PhD in Engineering from the Technical University of Munich, Germany. In his free time he loves to go to the gym and play with his kids.

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Generative AI has become a common tool for enhancing and accelerating the creative process across various industries, including entertainment, advertising, and graphic design. It enables more personalized experiences for audiences and improves the overall quality of the final products.

One significant benefit of generative AI is creating unique and personalized experiences for users. For example, generative AI is used by streaming services to generate personalized movie titles and visuals to increase viewer engagement and build visuals for titles based on a user’s viewing history and preferences. The system then generates thousands of variations of a title’s artwork and tests them to determine which version most attracts the user’s attention. In some cases, personalized artwork for TV series significantly increased clickthrough rates and view rates as compared to shows without personalized artwork.

In this post, we demonstrate how you can use generative AI models like Stable Diffusion to build a personalized avatar solution on Amazon SageMaker and save inference cost with multi-model endpoints (MMEs) at the same time. The solution demonstrates how, by uploading 10–12 images of yourself, you can fine-tune a personalized model that can then generate avatars based on any text prompt, as shown in the following screenshots. Although this example generates personalized avatars, you can apply the technique to any creative art generation by fine-tuning on specific objects or styles.

Solution overview

The following architecture diagram outlines the end-to-end solution for our avatar generator.

The scope of this post and the example GitHub code we provide focus only on the model training and inference orchestration (the green section in the preceding diagram). You can reference the full solution architecture and build on top of the example we provide.

Model training and inference can be broken down into four steps:

1. Upload images to Amazon Simple Storage Service (Amazon S3). In this step, we ask you to provide a minimum of 10 high-resolution images of yourself. The more images, the better the result, but the longer it will take to train.
2. Fine-tune a Stable Diffusion 2.1 base model using SageMaker asynchronous inference. We explain the rationale for using an inference endpoint for training later in this post. The fine-tuning process starts with preparing the images, including face cropping, background variation, and resizing for the model. Then we use Low-Rank Adaptation (LoRA), a parameter-efficient fine-tuning technique for large language models (LLMs), to fine-tune the model. Finally, in postprocessing, we package the fine-tuned LoRA weights with the inference script and configuration files (tar.gz) and upload them to an S3 bucket location for SageMaker MMEs.
3. Host the fine-tuned models using SageMaker MMEs with GPU. SageMaker will dynamically load and cache the model from the Amazon S3 location based on the inference traffic to each model.
4. Use the fine-tuned model for inference. After the Amazon Simple Notification Service (Amazon SNS) notification indicating the fine-tuning is sent, you can immediately use that model by supplying a target_model parameter when invoking the MME to create your avatar.

We explain each step in more detail in the following sections and walk through some of the sample code snippets.

Prepare the images

To achieve the best results from fine-tuning Stable Diffusion to generate images of yourself, you typically need to provide a large quantity and variety of photos of yourself from different angles, with different expressions, and in different backgrounds. However, with our implementation, you can now achieve a high-quality result with as few as 10 input images. We have also added automated preprocessing to extract your face from each photo. All you need is to capture the essence of how you look clearly from multiple perspectives. Include a front-facing photo, a profile shot from each side, and photos from angles in between. You should also include photos with different facial expressions like smiling, frowning, and a neutral expression. Having a mix of expressions will allow the model to better reproduce your unique facial features. The input images dictate the quality of avatar you can generate. To make sure this is done properly, we recommend an intuitive front-end UI experience to guide the user through the image capture and upload process.

The following are example selfie images at different angles with different facial expressions.

Fine-tune a Stable Diffusion model

After the images are uploaded to Amazon S3, we can invoke the SageMaker asynchronous inference endpoint to start our training process. Asynchronous endpoints are intended for inference use cases with large payloads (up to 1 GB) and long processing times (up to 1 hour). It also provides a built-in queuing mechanism for queuing up requests, and a task completion notification mechanism via Amazon SNS, in addition to other native features of SageMaker hosting such as auto scaling.

Even though fine-tuning is not an inference use case, we chose to utilize it here in lieu of SageMaker training jobs due to its built-in queuing and notification mechanisms and managed auto scaling, including the ability to scale down to 0 instances when the service is not in use. This allows us to easily scale the fine-tuning service to a large number of concurrent users and eliminates the need to implement and manage the additional components. However, it does come with the drawback of the 1 GB payload and 1 hour maximum processing time. In our testing, we found that 20 minutes is sufficient time to get reasonably good results with roughly 10 input images on an ml.g5.2xlarge instance. However, SageMaker training would be the recommended approach for larger-scale fine-tuning jobs.

To host the asynchronous endpoint, we must complete several steps. The first is to define our model server. For this post, we use the Large Model Inference Container (LMI). LMI is powered by DJL Serving, which is a high-performance, programming language-agnostic model serving solution. We chose this option because the SageMaker managed inference container already has many of the training libraries we need, such as Hugging Face Diffusers and Accelerate. This greatly reduces the amount of work required to customize the container for our fine-tuning job.

The following code snippet shows the version of the LMI container we used in our example:

inference_image_uri = (
    f"763104351884.dkr.ecr.{region}.amazonaws.com/djl-inference:0.21.0-deepspeed0.8.3-cu117"
)
print(f"Image going to be used is ---- > {inference_image_uri}")

In addition to that, we need to have a serving.properties file that configures the serving properties, including the inference engine to use, the location of the model artifact, and dynamic batching. Lastly, we must have a model.py file that loads the model into the inference engine and prepares the data input and output from the model. In our example, we use the model.py file to spin up the fine-tuning job, which we explain in greater detail in a later section. Both the serving.properties and model.py files are provided in the training_service folder.

The next step after defining our model server is to create an endpoint configuration that defines how our asynchronous inference will be served. For our example, we are just defining the maximum concurrent invocation limit and the output S3 location. With the ml.g5.2xlarge instance, we have found that we are able to fine-tune up to two models concurrently without encountering an out-of-memory (OOM) exception, and therefore we set max_concurrent_invocations_per_instance to 2. This number may need to be adjusted if we’re using a different set of tuning parameters or a smaller instance type. We recommend setting this to 1 initially and monitoring the GPU memory utilization in Amazon CloudWatch.

# create async endpoint configuration
async_config = AsyncInferenceConfig(
    output_path=f"s3://{bucket}/{s3_prefix}/async_inference/output" , # Where our results will be stored
    max_concurrent_invocations_per_instance=2,
    notification_config={
      "SuccessTopic": "...",
      "ErrorTopic": "...",
    }, #  Notification configuration
)

Finally, we create a SageMaker model that packages the container information, model files, and AWS Identity and Access Management (IAM) role into a single object. The model is deployed using the endpoint configuration we defined earlier:

model = Model(
    image_uri=image_uri,
    model_data=model_data,
    role=role,
    env=env
)

model.deploy(
    initial_instance_count=1,
    instance_type=instance_type,
    endpoint_name=endpoint_name,
    async_inference_config=async_inference_config
)

predictor = sagemaker.Predictor(
    endpoint_name=endpoint_name,
    sagemaker_session=sagemaker_session
)

When the endpoint is ready, we use the following sample code to invoke the asynchronous endpoint and start the fine-tuning process:

sm_runtime = boto3.client("sagemaker-runtime")

input_s3_loc = sess.upload_data("data/jw.tar.gz", bucket, s3_prefix)

response = sm_runtime.invoke_endpoint_async(
    EndpointName=sd_tuning.endpoint_name,
    InputLocation=input_s3_loc)

For more details about LMI on SageMaker, refer to Deploy large models on Amazon SageMaker using DJLServing and DeepSpeed model parallel inference.

After invocation, the asynchronous endpoint starts queueing our fine-tuning job. Each job runs through the following steps: prepare the images, perform Dreambooth and LoRA fine-tuning, and prepare the model artifacts. Let’s dive deeper into the fine-tuning process.

Prepare the images

As we mentioned earlier, the quality of input images directly impacts the quality of fine-tuned model. For the avatar use case, we want the model to focus on the facial features. Instead of requiring users to provide carefully curated images of exact size and content, we implement a preprocessing step using computer vision techniques to alleviate this burden. In the preprocessing step, we first use a face detection model to isolate the largest face in each image. Then we crop and pad the image to the required size of 512 x 512 pixels for our model. Finally, we segment the face from the background and add random background variations. This helps highlight the facial features, allowing our model to learn from the face itself rather than the background. The following images illustrate the three steps in this process.

Step 1: Face detection using computer vision	Step 2: Crop and pad the image to 512 x 512 pixels	Step 3 (Optional): Segment and add background variation

Dreambooth and LoRA fine-tuning

For fine-tuning, we combined the techniques of Dreambooth and LoRA. Dreambooth allows you to personalize your Stable Diffusion model, embedding a subject into the model’s output domain using a unique identifier and expanding the model’s language vision dictionary. It uses a method called prior preservation to preserve the model’s semantic knowledge of the class of the subject, in this case a person, and use other objects in the class to improve the final image output. This is how Dreambooth can achieve high-quality results with just a few input images of the subject.

The following code snippet shows the inputs to our trainer.py class for our avatar solution. Notice we chose <<TOK>> as the unique identifier. This is purposely done to avoid picking a name that may already be in the model’s dictionary. If the name already exists, the model has to unlearn and then relearn the subject, which may lead to poor fine-tuning results. The subject class is set to “a photo of person”, which enables prior preservation by first generating photos of people to feed in as additional inputs during the fine-tuning process. This will help reduce overfitting as model tries to preserve the previous knowledge of a person using the prior preservation method.

status = trn.run(base_model="stabilityai/stable-diffusion-2-1-base",
    resolution=512,
    n_steps=1000,
    concept_prompt="photo of <<TOK>>", # << unique identifier of the subject
    learning_rate=1e-4,
    gradient_accumulation=1,
    fp16=True,
    use_8bit_adam=True,
    gradient_checkpointing=True,
    train_text_encoder=True,
    with_prior_preservation=True,
    prior_loss_weight=1.0,
    class_prompt="a photo of person", # << subject class
    num_class_images=50,
    class_data_dir=class_data_dir,
    lora_r=128,
    lora_alpha=1,
    lora_bias="none",
    lora_dropout=0.05,
    lora_text_encoder_r=64,
    lora_text_encoder_alpha=1,
    lora_text_encoder_bias="none",
    lora_text_encoder_dropout=0.05
)

A number of memory-saving options have been enabled in the configuration, including fp16, use_8bit_adam, and gradient accumulation. This reduces the memory footprint to under 12 GB, which allows for fine-tuning of up to two models concurrently on an ml.g5.2xlarge instance.

LoRA is an efficient fine-tuning technique for LLMs that freezes most of the weights and attaches a small adapter network to specific layers of the pre-trained LLM, allowing for faster training and optimized storage. For Stable Diffusion, the adapter is attached to the text encoder and U-Net components of the inference pipeline. The text encoder converts the input prompt to a latent space that is understood by the U-Net model, and the U-Net model uses the latent meaning to generate the image in the subsequent diffusion process. The output of the fine-tuning is just the text_encoder and U-Net adapter weights. At inference time, these weights can be reattached to the base Stable Diffusion model to reproduce the fine-tuning results.

The figures below are detail diagram of LoRA fine-tuning provided by original author: Cheng-Han Chiang, Yung-Sung Chuang, Hung-yi Lee, “AACL_2022_tutorial_PLMs,” 2022

By combining both methods, we were able to generate a personalized model while tuning an order-of-magnitude fewer parameters. This resulted in a much faster training time and reduced GPU utilization. Additionally, storage was optimized with the adapter weight being only 70 MB, compared to 6 GB for a full Stable Diffusion model, representing a 99% size reduction.

Prepare the model artifacts

After fine-tuning is complete, the postprocessing step will TAR the LoRA weights with the rest of the model serving files for NVIDIA Triton. We use a Python backend, which means the Triton config file and the Python script used for inference are required. Note that the Python script has to be named model.py. The final model TAR file should have the following file structure:

|--sd_lora
   |--config.pbtxt
   |--1
      |--model.py
      |--output #LoRA weights
         |--text_encoder
         |--unet
         |--train.sh

Host the fine-tuned models using SageMaker MMEs with GPU

After the models have been fine-tuned, we host the personalized Stable Diffusion models using a SageMaker MME. A SageMaker MME is a powerful deployment feature that allows hosting multiple models in a single container behind a single endpoint. It automatically manages traffic and routing to your models to optimize resource utilization, save costs, and minimize operational burden of managing thousands of endpoints. In our example, we run on GPU instances, and SageMaker MMEs support GPU using Triton Server. This allows you to run multiple models on a single GPU device and take advantage of accelerated compute. For more detail on how to host Stable Diffusion on SageMaker MMEs, refer to Create high-quality images with Stable Diffusion models and deploy them cost-efficiently with Amazon SageMaker.

For our example, we made additional optimization to load the fine-tuned models faster during cold start situations. This is possible because of LoRA’s adapter design. Because the base model weights and Conda environments are the same for all fine-tuned models, we can share these common resources by pre-loading them onto the hosting container. This leaves only the Triton config file, Python backend (model.py), and LoRA adaptor weights to be dynamically loaded from Amazon S3 after the first invocation. The following diagram provides a side-by-side comparison.

This significantly reduces the model TAR file from approximately 6 GB to 70 MB, and therefore is much faster to load and unpack. To do the preloading in our example, we created a utility Python backend model in models/model_setup. The script simply copies the base Stable Diffusion model and Conda environment from Amazon S3 to a common location to share across all the fine-tuned models. The following is the code snippet that performs the task:

def initialize(self, args):
          
        #conda env setup
        self.conda_pack_path = Path(args['model_repository']) / "sd_env.tar.gz"
        self.conda_target_path = Path("/tmp/conda")
        
        self.conda_env_path = self.conda_target_path / "sd_env.tar.gz"
             
        if not self.conda_env_path.exists():
            self.conda_env_path.parent.mkdir(parents=True, exist_ok=True)
            shutil.copy(self.conda_pack_path, self.conda_env_path)
        
        #base diffusion model setup
        self.base_model_path = Path(args['model_repository']) / "stable_diff.tar.gz"
        
        try:
            with tarfile.open(self.base_model_path) as tar:
                tar.extractall('/tmp')
                
            self.response_message = "Model env setup successful."
        
        except Exception as e:
            # print the exception message
            print(f"Caught an exception: {e}")
            self.response_message = f"Caught an exception: {e}"

Then each fine-tuned model will point to the shared location on the container. The Conda environment is referenced in the config.pbtxt.

name: "pipeline_0"
backend: "python"
max_batch_size: 1

...

parameters: {
  key: "EXECUTION_ENV_PATH",
  value: {string_value: "/tmp/conda/sd_env.tar.gz"}
}

The Stable Diffusion base model is loaded from the initialize() function of each model.py file. We then apply the personalized LoRA weights to the unet and text_encoder model to reproduce each fine-tuned model:

...

class TritonPythonModel:

    def initialize(self, args):
        self.output_dtype = pb_utils.triton_string_to_numpy(
            pb_utils.get_output_config_by_name(json.loads(args["model_config"]),
                                               "generated_image")["data_type"])
        
        self.model_dir = args['model_repository']
    
        device='cuda'
        self.pipe = StableDiffusionPipeline.from_pretrained('/tmp/stable_diff',
                                                            torch_dtype=torch.float16,
                                                            revision="fp16").to(device)
                                                            
        # Load the LoRA weights
        self.pipe.unet = PeftModel.from_pretrained(self.pipe.unet, unet_sub_dir)

        if os.path.exists(text_encoder_sub_dir):
            self.pipe.text_encoder = PeftModel.from_pretrained(self.pipe.text_encoder, text_encoder_sub_dir)

import random

prompt = """<<TOK>> epic portrait, zoomed out, blurred background cityscape, bokeh,
 perfect symmetry, by artgem, artstation ,concept art,cinematic lighting, highly 
 detailed, octane, concept art, sharp focus, rockstar games, post processing, 
 picture of the day, ambient lighting, epic composition"""

negative_prompt = """
beard, goatee, ugly, tiling, poorly drawn hands, poorly drawn feet, poorly drawn face, out of frame, extra limbs, disfigured, deformed, body out of frame, blurry, bad anatomy, blurred, 
watermark, grainy, signature, cut off, draft, amateur, multiple, gross, weird, uneven, furnishing, decorating, decoration, furniture, text, poor, low, basic, worst, juvenile, 
unprofessional, failure, crayon, oil, label, thousand hands
"""

seed = random.randint(1, 1000000000)

gen_args = json.dumps(dict(num_inference_steps=50, guidance_scale=7, seed=seed))

inputs = dict(prompt = prompt, 
              negative_prompt = negative_prompt, 
              gen_args = gen_args)

payload = {
    "inputs":
        [{"name": name, "shape": [1,1], "datatype": "BYTES", "data": [data]} for name, data in inputs.items()]
}

response = sm_runtime.invoke_endpoint(
    EndpointName=endpoint_name,
    ContentType="application/octet-stream",
    Body=json.dumps(payload),
    TargetModel="sd_lora.tar.gz",
)
output = json.loads(response["Body"].read().decode("utf8"))["outputs"]
original_image = decode_image(output[0]["data"][0])
original_image

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