Posts in category: Amazon AWS

Legal professionals often spend a significant portion of their work searching through and analyzing large documents to draw insights, prepare arguments, create drafts, and compare documents. The rise of generative artificial intelligence (AI) has brought an inflection of foundation models (FMs). These FMs, with simple instructions (prompts), can perform various tasks such as drafting emails, extracting key terms from contracts or briefs, summarizing documents, searching through multiple documents, and more. As a result, these models are fit for legal tech. Goldman Sachs estimated that generative AI could automate 44% of legal tasks in the US. A special report published by Thompson Reuters reported that generative AI awareness is significantly higher among legal professionals, with 91% of respondents saying they have heard of or read about these tools.

However, such models alone are not sufficient due to legal and ethical concerns around data privacy. Security and confidentiality are of paramount importance in the legal field. Legal tech professionals, like any other business handling sensitive customer information, require robust security and confidentiality practices. Advancements in AI and natural language processing (NLP) show promise to help lawyers with their work, but the legal industry also has valid questions around the accuracy and costs of these new techniques, as well as how customer data will be kept private and secure. AWS AI and machine learning (ML) services help address these concerns within the industry.

In this post, we share how legal tech professionals can build solutions for different use cases with generative AI on AWS.

AI/ML on AWS

AI and ML have been a focus for Amazon for over 25 years, and many of the capabilities customers use with Amazon are driven by ML. Ecommerce recommendation engines, Just Walk Out technology, Alexa devices, and route optimizations are some examples. These capabilities are built using the AWS Cloud. At AWS, we have played a key role in and making ML accessible to anyone who wants to use it, including more than 100,000 customers of all sizes and industries. Thomson Reuters, Booking.com, and Merck are some of the customers who are using the generative AI capabilities of AWS services to deliver innovative solutions.

AWS makes it straightforward to build and scale generative AI customized for your data, your use cases, and your customers. AWS gives you the flexibility to choose different FMs that work best for your needs. Your organization can use generative AI for various purposes like chatbots, intelligent document processing, media creation, and product development and design. You can now apply that same technology to the legal field.

When you’re building generative AI applications, FMs are part of the architecture and not the entire solution. There are other components involved, such as knowledge bases, data stores, and document repositories. It’s important to understand how your enterprise data is integrating with different components and the controls that can be put in place.

Security and your data on AWS

Robust security and confidentiality are foundations to the legal tech domain. At AWS, security is our top priority. AWS is architected to be the most secure global cloud infrastructure on which to build, migrate, and manage applications and workloads. This is backed by our deep set of over 300 cloud security tools and the trust of our millions of customers, including the most security sensitive organizations like government, healthcare, and financial services.

Security is a shared responsibility model. Core security disciplines, like identity and access management, data protection, privacy and compliance, application security, and threat modeling, are still critically important for generative AI workloads, just as they are for any other workload. For example, if your generative AI applications is accessing a database, you’ll need to know what the data classification of the database is, how to protect that data, how to monitor for threats, and how to manage access. But beyond emphasizing long-standing security practices, it’s crucial to understand the unique risks and additional security considerations that generative AI workloads bring. To learn more, refer to Securing generative AI: An introduction to the Generative AI Security Scoping Matrix.

Sovereignty has been a priority for AWS since the very beginning, when we were the only major cloud provider to allow you to control the location and movement of your customer data and address stricter data residency requirements. The AWS Digital Sovereignty Pledge is our commitment to offering AWS customers the most advanced set of sovereignty controls and features available in the cloud. We are committed to expanding our capabilities to allow you to meet your digital sovereignty needs, without compromising on the performance, innovation, security, or scale of the AWS Cloud.

AWS generative AI approach for legal tech

AWS solutions enable legal professionals to refocus their expertise on high-value tasks. On AWS, generative AI solutions are now within reach for legal teams of all sizes. With virtually unlimited cloud computing capacity, the ability to fine-tune models for specific legal tasks, and services tailored for confidential client data, AWS provides the ideal environment for applying generative AI in legal tech.

In the following sections, we share how we’re working with several legal customers on different use cases that are focused on improving the productivity of various tasks in legal firms.

Boost productivity to allow a search based on context and conversational Q&A

Legal professionals store their information in different ways, such as on premises, in the cloud, or a combination of the two. It can take hours or days to consolidate the documents prior to reviewing them if they are scattered across different locations. The industry relies on tools where searching is limited to each domain, and may not flexible enough for users to search for information.

To address this issue, AWS used AI/ML and search engines to provide a managed service where users can ask a human-like, open-ended generative AI-powered assistant to answer questions based on data and information. Users can prompt the assistant to extract key attributes that serve as metadata, find relevant documents, and answer legal questions and terms inquiries. What used to take hours can now be done in a matter of minutes, and based on what we have learned with our customers, AWS generative AI has been able to improve productivity of resources by up to a 15% increase compared to manual processes during its initial phases.

Boost productivity with legal document summarization

Legal tech workers can realize a benefit from the generation of first draft that can then be reviewed and revised by the process owner. Multiple use cases are being implemented under this category:

• Contract summarization for tax approval
• Approval attachment summarization
• Case summarization

The summarization of documents can either use existing documents and videos from your document management system or allow users to upload a document and ask questions in real time. Instead of writing the summary, generative AI uses FMs to create the content so the lawyer can review the final content. This approach reduces these laborious tasks to 5–10 minutes instead of 20–60 minutes.

Boost attorney productivity by drafting and reviewing legal documents using generative AI

Generative AI can help boost attorney productivity by automating the creation of legal documents. Tasks like drafting contracts, briefs, and memos can be time-consuming for attorneys. With generative AI, attorneys can describe the key aspects of a document in plain language and instantly generate an initial draft. This new approach uses generative AI to use templates and chatbot interactions to add allowed text to an initial validation prior to legal review.

Another use case is to improve reviewing contracts using generative AI. Attorneys spend valuable time negotiating contracts. Generative AI can streamline this process by reviewing and redlining contracts, and identify potential discrepancies and conflicting provisions. Given a set of documents, this functionality allows attorneys to ask open-ended questions based on the documents along with follow-up questions, enabling human-like conversational experiences with enterprise data.

Start your AWS generative AI journey today

We are at the beginning of a new and exciting foray into generative AI, and we have just scratched the surface of some potential applications in the legal field—from text summarization, drafting legal documents, or searching based on context. The AWS generative AI stack offers you the infrastructure to build and train your own FMs, services to build with existing FMs, or applications that use other FMs. You can start with the following services:

• Amazon Q Business is a new type of generative AI-powered assistant. It can be tailored to your business to have conversations, solve problems, generate content, and take actions using the data and expertise found in your company’s information repositories, code bases, and enterprise systems. Amazon Q Business provides quick, relevant, and actionable information and advice to help streamline tasks, speed up decision-making and problem-solving, and help spark creativity and innovation.
• Amazon Bedrock is a fully managed service that offers a choice of high-performing FMs from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI. With Amazon Bedrock, you can experiment with and evaluate top FMs for your use case, privately customize them with your data using techniques such as fine-tuning and Retrieval Augmented Generation (RAG), and build agents that perform tasks using your enterprise systems and data sources.

In upcoming posts, we will dive deeper into different architectural patterns that describe how to use AWS generative AI services to solve for these different use cases.

Conclusion

Generative AI solutions are empowering legal professionals to reduce the difficulty in finding documents and performing summarization, and allow your business to standardize and modernize contract generation and revisions. These solutions do not envision to replace law experts, but instead increase their productivity and time working on practicing law.

We are excited about how legal professionals can build with generative AI on AWS. Start exploring our services and find out where generative AI could benefit your organization. Our mission is to make it possible for developers of all skill levels and for organizations of all sizes to innovate using generative AI in a secure and scalable manner. This just the beginning of what we believe will be the next wave of generative AI, powering new possibilities in legal tech.

Resources

• Securing generative AI: An introduction to the Generative AI Security Scoping Matrix
• AWS Security Reference Architecture (AWS SRA)
• AWS Responsible AI

About the Authors

Victor Fiss a Sr. Solution Architect Leader at AWS, helping customers in their cloud journey from infrastructure to generative AI solutions at scale. In his free time, he enjoys hiking and playing with his family.

Vineet Kachhawaha is a Sr. Solutions Architect at AWS focusing on AI/ML and generative AI. He co-leads the AWS for Legal Tech team within AWS. He is passionate about working with enterprise customers and partners to design, deploy, and scale AI/ML applications to derive business value.

Pallavi Nargund is a Principal Solutions Architect at AWS. She is a generative AI lead for East – Greenfield. She leads the AWS for Legal Tech team. She is passionate about women in technology and is a core member of Women in AI/ML at Amazon. She speaks at internal and external conferences such as AWS re:Invent, AWS Summits, and webinars. Pallavi holds a Bachelor’s of Engineering from the University of Pune, India. She lives in Edison, New Jersey, with her husband, two girls, and a Labrador pup.

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This post is co-written with Vraj Shah and Chaitanya Hari from DoorDash.

DoorDash connects consumers with their favorite local businesses in more than 30 countries across the globe. Recently, they faced a significant challenge in handling the high volume of calls from its contractor delivery workers, known as Dashers. With a user base of over 37 million active consumers and 2 million monthly active Dashers at the end of 2023, the company recognized the need to reduce the burden on its live agents by providing a more efficient self-service experience for Dashers.

To address this challenge, the contact center team at DoorDash wanted to harness the power of generative AI to deploy a solution quickly, and at scale, while maintaining their high standards for issue resolution and customer satisfaction. Dashers, who generally prefer calling into support rather than texting while they’re on the road, require fast and reliable assistance, with minimal response latency. This low latency requirement became a critical factor in DoorDash’s quest for an effective, voice-enabled self-service solution.

Working with the AWS Generative AI Innovation Center, DoorDash built a solution to provide Dashers with a low-latency self-service voice experience to answer frequently asked questions, reducing the need for live agent assistance, in just 2 months.

The solution uses Amazon Lex, a voice-enabled conversational AI service; Amazon Bedrock, a fully managed service that makes foundation models (FMs) from leading AI startups and Amazon available through an API, so you can choose from a wide range of FMs to find the model that is best suited for your use case; and Amazon Bedrock Knowledge Bases, a fully managed service that connects large language models (LLMs) to your data sources. It’s a fully serverless architecture that uses Amazon OpenSearch Serverless, which can run petabyte-scale workloads, without you having to manage the underlying infrastructure.

The solution fields hundreds of thousands of calls per day, responding to Dashers with answers to their questions in 2.5 seconds or less. It also enables operational capabilities including automated testing, conversation analytics, monitoring and observability, and LLM hallucination prevention and detection.

“We wanted to empower Dashers to get help with their most common questions and issues as quickly and efficiently as possible, saving them time, effort, and increasing their trust in DoorDash’s self-service capabilities,”

– Chaitanya Hari, Contact Center Product Lead at DoorDash.

In this post, we show how you can deploy generative AI agents in your contact center using AWS services.

Solution overview

For this post, we use a fictional hotel chain example called Example Corp Hospitality Group. The following is a sample interaction between a customer and the LLM-powered automated agent:

Customer: Hi, we’re looking for a great place for a family vacation

Agent: Example Corp Family Getaways offers family-friendly hotels and resorts located near top attractions like theme parks, national parks, and historic sites. The resorts feature spacious accommodations, activities for all ages, and diverse dining options to create unforgettable family memories.

Customer: Where are they located?

Agent: Example Corp Family Getaways offers family-friendly resorts in a variety of popular vacation destinations, including Orlando, Florida; Anaheim, California; Williamsburg, Virginia; Pigeon Forge, Tennessee; San Antonio, Texas; Scottsdale, Arizona; Branson, Missouri; Wisconsin Dells, Wisconsin; and Hershey, Pennsylvania.

Customer: Pigeon Forge sounds interesting.

Agent: Our Pigeon Forge resort offers families a chance to experience the great outdoors while enjoying the excitement of Dollywood and other nearby attractions. Guests can enjoy our indoor water park, mini-golf course, and nightly s’mores roasts around the fire pit.

Customer: Do they have kids’ menus?

Agent: Example Corp Family Getaways resorts in Pigeon Forge, Tennessee feature family-friendly dining options, including dedicated kids’ menus with favorites like pizza, chicken nuggets, and mac and cheese.

You can deploy the solution in your own AWS account and try the example solution. The following diagram illustrates the solution architecture.

We will walk you through deploying and testing these major components of the solution:

1. An AWS CloudFormation stack to set up an Amazon Bedrock knowledge base, where you store the content used by the solution to answer questions.
2. A CloudFormation stack to create an Amazon Lex bot and an AWS Lambda fulfillment function, which implement the core Retrieval Augmented Generation (RAG) question answering capability.
3. An optional CloudFormation stack to deploy a data pipeline to enable a conversation analytics dashboard.
4. An optional CloudFormation stack to enable an asynchronous LLM hallucination detection feature.
5. Optional Jupyter notebooks in Amazon SageMaker that provide an automated testing capability that compares generated answers to ground truth answers, providing pass/fail grades with explanations.

Everything you need is also provided as open source in our GitHub repo.

Prerequisites

You need to have an AWS account and an AWS Identity and Access Management (IAM) role and user with permissions to create and manage the necessary resources and components for this application. If you don’t have an AWS account, see How do I create and activate a new Amazon Web Services account?

This solution uses Amazon Bedrock LLMs to find answers to questions from your knowledge base. Before proceeding, if you have not previously done so, request access to at least the following Amazon Bedrock models:

• Amazon Titan Embeddings G1 – Text
• Cohere Embed English v3 and Cohere Embed Multilingual v3
• Anthropic’s Claude 3 Haiku and Anthropic’s Claude 3 Sonnet

If you’ll be integrating with Amazon Connect, make sure you have an instance available in your account. If you don’t already have one, you can create one. If you plan to deploy the conversation analytics stack, you need Amazon QuickSight, so make sure you have enabled it in your AWS account. 

At the time of writing, this solution is available in the following AWS Regions: Asia Pacific (Singapore, Sydney, Tokyo), Canada (Central), Europe (Frankfurt, London), US East (N. Virginia), and US West (Oregon).

Deploy the Amazon Bedrock knowledge base

You can use the provided CloudFormation stack for the Amazon Bedrock knowledge base instances you may need using Amazon Simple Storage Service (Amazon S3) as a data source. Complete the following steps to set up your knowledge base:

1. Sign in to your AWS account, then choose Launch Stack to deploy the CloudFormation template:

2. Provide a stack name, for example contact-center-kb.
3. Provide the name for an existing S3 bucket, for example contact-center-kb-(your-account-number). This is where the content for the demo solution will be stored. Create this S3 bucket if you don’t already have one.
4. Do not specify an S3 prefix.
5. Choose an embedding model, such as amazon.titan-embed-text-v2:0.
6. Choose the Fixed-sized chunking chunking strategy.
7. For the maximum tokens per chunk entry, use 600 for the Amazon Titan embeddings model. (If you are using the Cohere embeddings model, use 512). This represents about a full page of text.
8. For the percentage overlap, use 10%.
9. Leave the four entries for Index Details at their default values (index name, vector field name, metadata field name, and text field name).
10. Choose Next.
11. On the Configure stack options page, choose Next
12. On the Review and create page, acknowledge the IAM capabilities message and choose Submit.

The stack will take about 10 minutes to deploy.

Upload the sample content and test your knowledge base

The demonstration sample for the solution includes an LLM-based hotel-bot that can answer questions about the fictional hotel chain Example Corp Hospitality Group. You need to load the content for this hotel chain into the S3 bucket that you specified for the knowledge base stack. You can find the S3 bucket used by the CloudFormation stack on the Outputs tab for the stack.

1. Either using the AWS Command Line Interface (AWS CLI) or the AWS Management Console, upload the following folders from the content section of the GitHub repo:

• • corporate
  • family-getaways
  • luxury-suites
  • party-times
  • seaside-resorts
  • waypoint-inns

You can choose either the PDF versions or the Word document versions (Word versions recommended). When you’re done, the top level of your S3 bucket should contain six folders, each containing a single Word or PDF document.

2. On the Amazon Bedrock console, choose Knowledge bases in the navigation pane.
3. Choose your new knowledge base to open it.

A message appears that says “One or more data sources have not been synced.”

4. Select the data source and choose Sync.

The sync process should only take a minute or two.

After your data source has been synced, you can try some question answering on the Amazon Bedrock console. Make sure you have enabled all the models approved by your organization on the Amazon Bedrock Model access page.

Select an LLM model, such as Anthropic’s Claude 3 Haiku on Amazon Bedrock, and start asking questions! You might want to peruse the sample documents you uploaded for some ideas about questions to ask.

Deploy the hallucination detection stack (optional)

If you want to use the optional asynchronous hallucination detection feature, deploy this stack. Otherwise, move on to the next section. You can use this CloudFormation stack for any RAG-based solution requiring asynchronous hallucination detection.

1. Choose Launch Stack:

2. Provide a stack name, for example contact-center-hallucination-detection.
3. Specify an LLM to perform the hallucination detection. At the time of writing, there are seven LLMs that are recommended for hallucination detection. For the demo solution, choose the default (Claude V3 Sonnet).
4. Optionally, create an Amazon Key Management Service (AWS KMS) customer managed key (CMK) to encrypt the Amazon Simple Queue Service (Amazon SQS) queue and the Amazon CloudWatch Logs log group for the Lambda function (recommended for production).

There are two types of Amazon CloudWatch alarms in this stack:

• ERROR alarms – For code issues with the Lambda function that does the hallucination detection work
• WARNING alarms – For when the Lambda function actually detects a hallucination

Both alarm types are optional, but recommended.

5. Choose yes to enable or no to disable the alarms.
6. For the alarms that you enable, you can specify an optional email address or distribution list to receive email notifications about the alarms.
7. Choose Next.
8. On the Configure stack options page, choose Next
9. On the Review and create page, acknowledge the IAM capabilities message and choose Submit.

The stack will take about a minute or two to deploy.

When the stack is complete, you can review the resources it creates on the Resources tab for the CloudFormation stack. In particular, review the Lambda function code.

If you entered email addresses for the alarm notifications, you should receive email requests asking you to confirm the subscriptions. Confirm them to receive email notifications about alarms that may occur.

Deploy the RAG solution stack

If you’re integrating with Amazon Connect, make sure you have an instance available in your account. If you don’t already have one, you can create one. Then complete the following steps to deploy the Amazon Lex bot and Lambda fulfillment function:

1. Choose Launch Stack:

2. Provide a stack name, for example contact-center-rag-solution.
3. Provide a name for the Amazon Lex bot, for example hotel-bot.
4. Specify the number of conversation turns to retain for context. This can be optimized for different use cases and datasets. For the hotel-bot demo, try the default of 4.
5. Optionally, specify an existing CloudWatch Logs log group ARN for the Amazon Lex conversation logs. You’ll need this if you’re planning to deploy the conversation analytics stack. Create a log group if you don’t already have one.
6. Optionally, enter a value for Lambda provisioned concurrency units for the Amazon Lex bot handler function. If set to a non-zero number, this will prevent Lambda cold starts and is recommended for production and for internal testing. For development, 0 or 1 is recommended.
7. Optionally, select the option to create a KMS CMK to encrypt the CloudWatch Logs log groups for the Lambda functions (recommended for production).
8. If you’re integrating with Amazon Connect, provide the Amazon Connect instance ARN, as well as the name for a new contact flow that the stack will create for you.
9. Provide the knowledge base ID from the knowledge base stack you just created. You can find this on the Outputs tab of the knowledge base stack.
10. Provide the S3 bucket used by the knowledge base stack (also referenced on the Outputs tab).
11. If you created the hallucination detection stack, enter the SQS queue name. You can find this on the Outputs tab of the hallucination detection stack.
12. If you opted for a KMS key for your hallucination detection stack, enter the KMS key ARN.
13. Choose Next.
14. On the Configure stack options page, choose Next
15. On the Review and create page, acknowledge the IAM capabilities message and choose Submit.

The stack will take a few minutes to complete.

To try the RAG solution, navigate to the Amazon Lex console and open the hotel-bot bot. The bot has a single language section for the English language. Choose Intents in the navigation pane to check out the intents for this sample bot. They include the following:

• Intents related to questions about the hotel chain and its various hotel brands – This includes Accommodations, Amenities, CorporateOverview, Locations, Parking, and more. These intents are routed to the RAG solution by Amazon Lex. Technically, intents like these could be omitted, allowing the FallbackIntent to handle requests of this nature. However, including these intents (and their sample utterances) provides Amazon Lex with information about the “language” of your solution domain, allowing it to better optimize its speech-to-text engine and improve speech transcription accuracy. In addition, including these intents is useful for conversation analytics.
• SwitchBrand – This intent is designed to improve conversation flow by allowing the user to say things like “What about at your other hotels?” in the middle of a conversation.
• Booking – This demonstrates an example of routing the caller to a live agent queue.
• SpeakToAgent – This intent is for when a caller specifically requests a live agent.
• Welcome, Goodbye, and Help – These conversation support intents are for starting and ending the conversation, or asking what the bot can do.
• FallbackIntent – This is the standard intent for questions or requests that don’t match other intents. In this example solution, such requests are also routed to the RAG solution to allow the LLM to answer based on the content in the knowledge base.
• SelectKnowledgeBase and SelectLLM – These allow the user to direct the RAG solution to use a different knowledge base instance (if more than one is available) or a different LLM. These intents are designed for testing purposes, and should normally be included only in non-production deployments. You can test the RAG solution with any of the LLMs available on Amazon Bedrock. You can also switch to a different knowledge base or LLM mid-conversation, if desired.
• ToggleLLMGuardrails and ToggleLLMContext – These allow the user to turn the prompt-based LLM guardrails off or on, and to disable or enable the retrieval of information from the knowledge base. These intents are designed for testing purposes, and should normally be included only in non-production environments. You can turn these settings off and on mid-conversation, if desired.

You can choose Test on the Amazon Lex console to try the solution.

Try some sample conversations, for example:

• Ask “We’re looking for a nice place for a family vacation” and the bot will respond “Example Corp Family Getaways offers family-friendly accommodations…”
• Ask “Where are they located?” and the bot will respond “Example Corp Family Getaways has locations in…”
• Ask “Tell me more about the one in Pigeon Forge” and the bot will respond “The Example Corp Family Getaways resort in Pigeon Forge, Tennessee is…”

You can refer to the sample documents you uploaded for some ideas about questions to ask.

If you deployed the hallucination detection stack, you can look at its assessment of the answers you got when you tested. From the hallucination detection stack details page, on the Resources tab, choose the HallucinationDetectionFunctionLogGroup entry. This opens the CloudWatch Logs log group for the Lambda hallucination detection function. You can inspect the log statements to observe the hallucination detection process in action, as shown in the following screenshot.

If you’re integrating with Amazon Connect, there will be a new contact flow in the Amazon Connect instance you specified, as shown in the following screenshot.

To test using voice, just claim a phone number, associate it with this contact flow, and give it a call!

Deploy the conversation analytics stack (optional)

This stack uses QuickSight for analytics, so make sure you have already enabled it in your AWS account before deploying this stack.

1. Choose Launch Stack:

2. Provide a stack name, for example contact-center-analytics.
3. Provide the name (not the ARN) of the Amazon Lex conversation logs log group. This is the same CloudWatch Logs log group you used for the the RAG solution CloudFormation stack.
4. Choose an option for purging source log streams from the log group. For testing, choose no.
5. Choose an option for redacting sensitive data using from the conversation logs. For testing, choose no.
6. Leave the personally identifiable information (PII) entity types and confidence score thresholds at their default values.
7. Choose an option for allowing unredacted logs for the Lambda function in the data pipeline. For testing, choose yes.
8. Select an option for creating a KMS CMK.

If you create a CMK, it will be used to encrypt the data in the S3 bucket that this stack creates, where the normalized conversation data is housed. This allows you to control which IAM principals are allowed to decrypt the data and view it. This setting is recommended for production.

9. Select the options for enabling CloudWatch alarms for ERRORS and WARNINGS in the Amazon Lex data pipeline. It is recommended to enable these alarms.
10. For the alarms that you enable, you can specify an optional email address or distribution list to receive email notifications about the alarms.
11. Choose Next.
12. On the Configure stack options page, choose Next
13. On the Review and create page, acknowledge the IAM capabilities message and choose Submit.

The stack should about 5 minutes to complete.

The following diagram illustrates the architecture of the stack.

As Amazon Lex writes conversation log entries to CloudWatch Logs (1), they are picked up by Amazon Data Firehose and streamed to an S3 bucket (2). Along the way, a Lambda transformation function (3) simplifies the JSON structure of the data to make it more user-friendly for querying purposes. The Lambda function can also redact sensitive data using Amazon Comprehend (4), and optionally purge the entries from the CloudWatch Logs log group as it consumes them.

On a scheduled basis (every 5 minutes), an AWS Glue crawler (5) inspects new data in the S3 bucket, and updates a data schema that is used by Amazon Athena (6) to provide a SQL interface to the data. This allows tools like QuickSight (7) to create near real-time dashboards, analytics, and visualizations of the data.

Set up the QuickSight dashboard (optional)

Before you create the QuickSight dashboard, make sure to return to the Amazon Lex console and ask a few questions, in order to generate some data for the dashboard. It will take about 5 minutes for the pipeline to process this new conversation data and make it available to QuickSight.

To set up dashboards and visualizations in QuickSight, complete the following steps:

1. On the QuickSight console, choose the user profile icon and choose Manage QuickSight.
2. Under Security & permissions, choose Manage in the QuickSight access to AWS services
3. Under Amazon S3, choose Select S3 buckets.
4. Enable access to the S3 bucket created by the conversation analytics stack (it will have a name with a 12-character unique identifier prepended to lex-conversation-logs). You don’t need to enable write permissions.
5. Choose Finish, then choose Save.
6. Choose the QuickSight menu icon to return to the main page in QuickSight.
7. In the navigation pane, choose Datasets.
8. Choose New dataset.
9. From the list of dataset sources, choose Athena.
10. Enter a data source name (for example contact-center-analytics).
11. Choose Create data source.
12. In the Choose your table window, choose your database, select your lex_conversation_logs table, and choose Edit/Preview data.

This opens your new QuickSight dataset. You can review the various attributes available, and see some results from your testing.

For improved speed in displaying the data, you can select the SPICE option for Query mode, but that will mean you need to refresh SPICE (or set up an hourly auto-update schedule) when you want to see data updates based on additional testing.

13. For now, leave the setting as Direct query.
14. When you’re ready, choose PUBLISH & VISUALIZE.
15. In the New sheet window, keep the defaults and choose CREATE.

This opens the analysis page, where you can start creating visualizations.

Automated testing notebooks (optional)

To try the automated testing capability, you need a SageMaker Jupyter notebook. Alternatively, you can run the notebooks locally in your integrated development environment (IDE) or other environment that supports Jupyter notebooks.

1. On the SageMaker console, under Notebook in the navigation pane, choose Notebook instances.
2. Choose Create notebook instance.
3. Give your notebook a name, such as contact-center-rag-testing.
4. To enable multi-threaded testing, it’s recommended to select a larger instance, such as ml.m5.2xlarge (which has 8 vCPUs) or ml.m5.4xlarge (which has 16 vCPUs). Don’t forget to stop them when they’re not in use.
5. Keep the default setting for Platform identifier (Amazon Linux 2, Jupyter Lab 3).
6. Under Additional configuration, increase the Volume size in GB setting to 50 GB.
7. In the Permissions and encryption section, under IAM role, choose Create a new role in the drop down list (don’t use the role creation wizard).
8. In the Create an IAM role window, you can specify any S3 buckets you want to provide access to (none are needed for this solution).
9. Choose Create role.

10. Choose Create notebook instance.

It will take several minutes for your notebook instance to become available. While it’s being created, you can update the IAM role to add some inline policies you’ll need for accessing Amazon Bedrock and Amazon Lex.

11. On the Notebook instances page, open your notebook instance (for example, contact-center-rag-testing) and then choose the entry under IAM role ARN to open the role.
12. Add the following inline policies (available in the notebooks/iam-roles folder in the GitHub repository):

• • bedrock-agents-retrieve.json
  • bedrock-invoke-model-all.json
  • lex-invoke-bot.json
  • opensearch-serverless-api-access.json

You can revise these roles to limit resource access as needed.

13. After your notebook instance has started, choose Open Jupyter to open the notebook.
14. Upload the following to your notebook instance (if desired, you can zip the files locally, upload the zip archive, and then unzip it in SageMaker):
    1. bedrock_helpers.py – This script configures LLM instances for the notebooks.
    2. bedrock_utils – You should make sure to upload all subfolders and files, and confirm that the folder structure is correct.
    3. run_tests.ipynb – This notebook runs a set of test cases.
    4. generate_ground_truths.ipynb – Given a set of questions, this notebook generates potential ground truth answers.
    5. test-runs – This folder should contain Excel workbooks.
15. Open the run_tests.ipynb notebook.
16. In the second cell, replace the bot_id and bot_alias_id values with the values for your Amazon Lex bot (you can find these on the Outputs tab of the RAG solution stack).
17. After you updated these values, choose Restart & Run All on the Kernel

If you’re using a ml.m5.2xlarge instance type, it should take about a minute to run the 50 test cases in the test-runs/test-cases-claude-haiku-2024-09-02.xlsx workbook. When it’s complete, you should find a corresponding test-results workbook in the test-runs folder in your notebook.

After a few minutes, you can also see the test results in your conversation analytics dashboard.

Adapt the solution to your use case

You can adapt this solution to your specific use cases with minimal work:

• Replace the Amazon Bedrock Knowledge Bases sample content with your content – Replace the content in the S3 bucket and organize it into a folder structure that makes sense for your use case. You can create a new knowledge base for your content.
• Replace the intents in the Amazon Lex bot with intents for your use case – Modify the Amazon Lex bot definition to reflect the interactions you want to enable for your use case.
• Modify the LLM prompts in the bedrock_utils code – In the Amazon Lex bot fulfillment Lambda function, review the LLM prompt definitions in the bedrock_utils folder. For example, provide a use case-specific definition for the role of the LLM-based agent.
• Modify the bot handler code if necessary – In the Amazon Lex bot fulfillment Lambda function, review the code in the TopicIntentHandler.py function. For the knowledge base search, this code provides an example that uses the sample hotel brands as topics. You can replace this metadata search query with one appropriate for your use cases.

Clean up

Congratulations! You have completed all the steps for setting up your voice-enabled contact center generative AI agent solution using AWS services.

When you no longer need the solution deployed in your AWS account, you can delete the CloudFormation stacks that you deployed, as well as the SageMaker notebook instance if you created one.

Conclusion

The contact center generative AI agent solution offers a scalable, cost-effective approach to automate Q&A conversations in your contact center, using AWS services like Amazon Bedrock, Amazon Bedrock Knowledge Bases, OpenSearch Serverless, and Amazon Lex.

The solution code is provided as open source—use it as a starting point for your own solution, and help us make it better by contributing back fixes and features through GitHub pull requests. Browse to the GitHub repository to explore the code, and check the CHANGELOG for the latest changes and the README for the latest documentation updates.

For expert assistance, the AWS Generative AI Innovation Center, AWS Professional Services, and our AWS Partners are here to help.

About the Authors

Vraj Shah is a Connect Developer at DoorDash.

Chaitanya Hari is a Voice/Contact Center Product Lead at DoorDash.

Marcelo Silva is a Principal Product Manager at Amazon Web Services, leading strategy and growth for Amazon Bedrock Knowledge Bases and Amazon Lex.

Adam Diesterhaft is a Sr. Pursuit Solutions Architect on the Amazon Connect team.

Brian Yost is a Principal Deep Learning Architect in the AWS Generative AI Innovation Center.

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This post is co-written with Deepali Rajale from Karini AI.

Karini AI, a leading generative AI foundation platform built on AWS, empowers customers to quickly build secure, high-quality generative AI apps. GenAI is not just a technology; it’s a transformational tool that is changing how businesses use technology. Depending on where they are in the adoption journey, the adoption of generative AI presents a significant challenge for enterprises. While pilot projects using Generative AI can start effortlessly, most enterprises need help progressing beyond this phase. According to Everest Research, more than a staggering 50% of projects do not move beyond the pilots as they face hurdles due to the absence of standardized or established GenAI operational practices.

Karini AI offers a robust, user-friendly GenAI foundation platform that empowers enterprises to build, manage, and deploy Generative AI applications. It allows beginners and expert practitioners to develop and deploy Gen AI applications for various use cases beyond simple chatbots, including agentic, multi-agentic, Generative BI, and batch workflows. The no-code platform is ideal for quick experimentation, building PoCs, and rapid transition to production with built-in guardrails for safety and observability for troubleshooting. The platform includes an offline and online quality evaluation framework to assess quality during experimentation and continuously monitor applications post-deployment. Karini AI’s intuitive prompt playground allows authoring prompts, comparison with different models across providers, prompt management, and prompt tuning. It supports iterative testing of more straightforward, agentic, and multi-agentic prompts. For production deployment, the no-code recipes enable easy assembly of the data ingestion pipeline to create a knowledge base and deployment of RAG or agentic chains. The platform owners can monitor costs and performance in real-time with detailed observability and seamlessly integrate with Amazon Bedrock for LLM inference, benefiting from extensive enterprise connectors and data preprocessing techniques.

The following diagram illustrates how Karini AI delivers a comprehensive Generative AI foundational platform encompassing the entire application lifecycle. This platform delivers a holistic solution that speeds up time to market and optimizes resource utilization by providing a unified framework for development, deployment, and management.

In this post, we share how Karini AI’s migration of vector embedding models from Kubernetes to Amazon SageMaker endpoints improved concurrency by 30% and saved over 23% in infrastructure costs.

Karini AI’s Data Ingestion Pipeline for creating vector embeddings

Enriching large language models (LLMs) with new data is crucial to building practical generative AI applications. This is where Retrieval Augmented Generation (RAG) comes into play. RAG enhances LLMs’ capabilities by incorporating external data and producing state-of-the-art performance in knowledge-intensive tasks. Karini AI offers no-code solutions for creating Generative AI applications using RAG. These solutions include two primary components: a data ingestion pipeline for building a knowledge base and a system for knowledge retrieval and summarization. Together, these pipelines simplify the development process, enabling the creation of powerful AI applications with ease.

Data Ingestion Pipeline

Ingesting data from diverse sources is essential for executing Retrieval Augmented Generation (RAG). Karini AI’s data ingestion pipeline enables connection to multiple data sources, including Amazon S3, Amazon Redshift, Amazon Relational Database Service (RDS), websites and Confluence, handling structured and unstructured data. This source data is pre-processed, chunked, and transformed into vector embeddings before being stored in a vector database for retrieval. Karini AI’s platform provides flexibility by offering a range of embedding models from their model hub, simplifying the creation of vector embeddings for advanced AI applications.

Here is a screenshot of Karini AI’s no-code data ingestion pipeline.

Karini AI’s model hub streamlines adding models by integrating with leading foundation model providers such as Amazon Bedrock and self-managed serving platforms.

Infrastructure challenges

As customers explore complex use cases and datasets grow in size and complexity, Karini AI scales the data ingestion process efficiently to provide high concurrency for creating vector embeddings using state-of-the-art embedding models, such as those listed in the MTEB leaderboard, which are rapidly evolving and unavailable on managed platforms.

Before migrating to Amazon SageMaker, we deployed our models on self-managed Kubernetes(K8s) on EC2 instances. Kubernetes offered significant flexibility to deploy models from HuggingFace quickly, but soon, our engineering had to manage many aspects of scaling and deployment. We faced the following challenges with our existing setup that must be addressed to improve efficiency and performance.

• Keeping up with SOTA(State-Of-The-Art) models: We managed different deployment manifests for each model type (such as classifiers, embeddings, and autocomplete), which was time-consuming and error-prone. We also had to maintain the logic to determine the memory allocation for different model types.
• Managing dynamic concurrency was hard: A significant challenge with using models hosted on Kubernetes was achieving the highest dynamic concurrency level. We aimed to maximize endpoint performance to achieve target transactions per second (TPS) while meeting strict latency requirements.
• Higher Costs: While Kubernetes (K8s) provides robust capabilities, it has become more costly due to the dynamic nature of data ingestion pipelines, which results in under-utilized instances and higher costs.

Our search for an inference platform led us to Amazon SageMaker, a solution that efficiently manages our models for higher concurrency, meets customer SLAs, and scales down serving when not needed. The reliability of SageMaker’s performance gave us confidence in its capabilities.

Amazon SageMaker for Model Serving

Choosing Amazon SageMaker was a strategic decision for Karini AI. It balanced the need for higher concurrencies at a lower cost, providing a cost-effective solution for our needs. SageMaker’s ability to scale and maximize concurrency while ensuring sub-second latency addresses various generative AI use cases making it a long-lasting investment for our platform.

Amazon SageMaker is a fully managed service that allows developers and data scientists to quickly build, train, and deploy machine learning (ML) models. With SageMaker, you can deploy your ML models on hosted endpoints and get real-time inference results. You can easily view the performance metrics for your endpoints in Amazon CloudWatch, automatically scale endpoints based on traffic, and update your models in production without losing any availability.

Karini AI’s data ingestion pipeline architecture with Amazon SageMaker Model endpoint is here.

Advantages of using SageMaker hosting

Amazon SageMaker offered our Gen AI ingestion pipeline many direct and indirect benefits.

1. Technical Debt Mitigation: Amazon SageMaker, being a managed service, allowed us to free our ML engineers from the burden of inference, enabling them to focus more on our core platform features—this relief from technical debt is a significant advantage of using SageMaker, reassuring us of its efficiency.
2. Meet customer SLAs: Knowledgebase creation is a dynamic task that may require higher concurrencies during vector embedding generation and minuscule load during query time. Based on customer SLAs and data volume, we can choose batch inference, real-time hosting with auto-scaling, or serverless hosting. Amazon SageMaker also provides recommendations for instance types suitable for embedding models.
3. Reduced Infrastructure cost: SageMaker is a pay-as-you-go service that allows you to create batch or real-time endpoints when there is demand and destroy them when work is complete. This approach reduced our infrastructure cost by more than 23% over the Kubernetes (K8s) platform.
4. SageMaker Jumpstart: SageMaker Jumpstart provides access to SOTA (State-Of-The-Art) models and optimized inference containers, making it ideal for creating new models that are accessible to our customers.
5. Amazon Bedrock compatibility: Karini AI integrates with Amazon Bedrock for LLM (Large Language Model) inference. The custom model import feature allows us to reuse the model weights used in SageMaker model hosting in Amazon Bedrock to maintain a joint code base and interchange serving between Bedrock and SageMaker as per the workload.

Conclusion

Karini AI significantly improved, achieving high performance and reducing model hosting costs by migrating to Amazon SageMaker. We can deploy custom third-party models to SageMaker and quickly make them available to Karini’s model hub for data ingestion pipelines. We can optimize our infrastructure configuration for model hosting as needed, depending on model size and our expected TPS. Using Amazon SagaMaker for model inference enabled Karini AI to handle increasing data complexities efficiently and meet concurrency needs while optimizing costs. Moreover, Amazon SageMaker allows easy integration and swapping of new models, ensuring that our customers can continuously leverage the latest advancements in AI technology without compromising performance or incurring unnecessary incremental costs.

Amazon SageMaker and Karini.ai offer a powerful platform to build, train, and deploy machine learning models at scale. By leveraging these tools, you can:

• Accelerate development:Build and train models faster with pre-built algorithms and frameworks.
• Enhance accuracy: Benefit from advanced algorithms and techniques for improved model performance.
• Scale effortlessly:Deploy models to production with ease and handle increasing workloads.
• Reduce costs:Optimize resource utilization and minimize operational overhead.

Don’t miss out on this opportunity to gain a competitive edge.

About Authors

Deepali Rajale is the founder of Karini AI, which is on a mission to democratize generative AI across enterprises. She enjoys blogging about Generative AI and coaching customers to optimize Generative AI practice. In her spare time, she enjoys traveling, seeking new experiences, and keeping up with the latest technology trends. You can find her on LinkedIn.

Ravindra Gupta is the Worldwide GTM lead for SageMaker and with a passion to help customers adopt SageMaker for their Machine Learning/ GenAI workloads.  Ravi is fond of learning new technologies, and enjoy mentoring startups on their Machine Learning practice. You can find him on Linkedin

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The climate crisis is one of the greatest challenges facing our world today. Its impacts are far-reaching, affecting every aspect of our lives—from public health and food security to economic stability and social justice. What’s more, the effects of climate change disproportionately burden the world’s most vulnerable populations, exacerbating existing inequities around gender, race, and socioeconomic status.

But we have the power to create change. By harnessing the transformative potential of AI, we can develop innovative solutions to tackle the intersectional challenges at the heart of the climate crisis. That’s why the International Research Centre on Artificial Intelligence (IRCAI), Zindi, and Amazon Web Services (AWS) are proud to announce the launch of the “AI for Equity Challenge: Climate Action, Gender, and Health”—a global virtual competition aimed at empowering organizations to use advanced AI and cloud technologies to drive real-world impact with a focus on benefitting vulnerable populations around the world.

Aligning with the United Nations Sustainable Development Goals (SDGs) 3, 5, and 13—focused on good health and well-being, gender equality, and climate action respectively—this challenge seeks to uncover the most promising AI-powered solutions that address the compounding issues of climate change, gender equity, and public health. By bringing together a diverse global community of innovators, we hope to accelerate the development of equitable, sustainable, and impactful applications of AI for the greater good.

“As artificial intelligence rapidly evolves, it is crucial that we harness its potential to address real-world challenges. At IRCAI, our mission is to guide the ethical development of AI technologies, ensuring they serve the greater good and are inclusive of marginalized AI communities. This challenge, in collaboration with AWS, is an opportunity to discover and support the most innovative minds that are using AI and advanced computing to create impactful solutions for the climate crisis.”

– Davor Orlic, COO at IRCAI.

The challenge will unfold in two phases, welcoming both ideators and solution builders to participate. In the first phase, organizations are invited to submit technical proposals outlining specific challenges at the intersection of climate action, gender equity, and health that they aim to address using AI and cloud technologies. A steering committee convened by IRCAI will evaluate these proposals based on criteria such as innovation, feasibility, and potential for global impact. The competition will be judged and mentored in collaboration with NAIXUS, a network of AI and sustainable development research organizations.

The top two winning proposals from the first phase will then advance to the second round, where they will serve as the foundation for two AI challenges hosted on the Zindi platform. During this phase, developers and data scientists from around the world will compete to build the most successful AI-powered solutions to tackle the real-world problems identified by the first-round winners.

AI for Equity Challenge Timeline

The winning AI solutions from the second-round challenges will belong entirely to the organizations that submitted the original winning proposals, who will also receive $15,000 in AWS credits and technical support from AWS and IRCAI to help implement their solutions. Additionally, the first-place teams in each of the two final Zindi challenges will receive cash prizes of $6,000, $4,000, and $2,500 for first, second, and third place respectively.

But the true reward goes beyond the prizes. By participating in this challenge, organizations and individuals alike will have the opportunity to make a lasting impact on the lives of those most vulnerable to the effects of climate change. Through the power of AI and advanced cloud computing, we can develop groundbreaking solutions that empower women, improve public health outcomes, and drive sustainable progress on the climate action front.

Throughout the hackathon, participants will have access to a wealth of resources, including mentorship from industry experts, training materials, and AWS cloud computing resources. Amazon Sustainability Data Initiative (ASDI), a collaboration between AWS and leading scientific organizations, provides a catalog of over 200 datasets spanning climate projections, satellite imagery, air quality data, and more, enabling participants to build robust and data-driven solutions.

“Climate change is one of the greatest threats of our time, and we believe innovation is key to overcoming it. The AI for Equity Challenge invites innovators to bring forward their most visionary ideas, and we’ll support them with AWS resources — whether that’s computing power or advanced cloud technologies — to turn those ideas into reality. Our goal is to drive cloud innovation, support sustainability solutions, and make a meaningful impact on the climate crisis.”

– Dave Levy, Vice President of Worldwide Public Sector, AWS

This initiative is made possible through the support of ASDI, which provides researchers, scientists, and innovators with access to a wealth of publicly available datasets on AWS to advance their sustainability-focused work. The AI for Equity Challenge: Climate Action, Gender, and Health is open for submissions from September 23 to November 4, 2024. The two winning proposals from the first round will be announced on December 2, 2024, with the final AI challenge winners revealed on February 12, 2025.

Don’t miss your chance to be part of the solution. Visit https://zindi.africa/ai-equity-challenge to learn more and submit your proposal today. Together, we can harness the power of AI to create a more sustainable, equitable, and just world for generations to come.

Visit http://zindi.africa/ai-equity-challenge to learn more and participate.

This contest is hosted in collaboration with:

About the author

Joe Fontaine is the Product marketing lead for AWS AI Builder Programs. He is passionate about making machine learning more accessible to all through hands-on educational experiences. Outside of work he enjoys freeride mountain biking, aerial cinematography, and exploring the wilderness with his family.

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Since its launch in 2018, Just Walk Out technology by Amazon has transformed the shopping experience by allowing customers to enter a store, pick up items, and leave without standing in line to pay. You can find this checkout-free technology in over 180 third-party locations worldwide, including travel retailers, sports stadiums, entertainment venues, conference centers, theme parks, convenience stores, hospitals, and college campuses. Just Walk Out technology’s end-to-end system automatically determines which products each customer chose in the store and provides digital receipts, eliminating the need for checkout lines.

In this post, we showcase the latest generation of Just Walk Out technology by Amazon, powered by a multi-modal foundation model (FM). We designed this multi-modal FM for physical stores using a transformer-based architecture similar to that underlying many generative artificial intelligence (AI) applications. The model will help retailers generate highly accurate shopping receipts using data from multiple inputs including a network of overhead video cameras, specialized weight sensors on shelves, digital floor plans, and catalog images of products. To put it in plain terms, a multi-modal model means using data from multiple inputs.

Our research and development (R&D) investments in state-of-the-art multi-modal FMs enables the Just Walk Out system to be deployed in a wide range of shopping situations with greater accuracy and at lower cost. Similar to large language models (LLMs) that generate text, the new Just Walk Out system is designed to generate an accurate sales receipt for every shopper visiting the store.

The challenge: Tackling complicated long-tail shopping scenarios

Because of their innovative checkout-free environment, Just Walk Out stores presented us with a unique technical challenge. Retailers and shoppers as well as Amazon demand nearly 100 percent checkout accuracy, even in the most complex shopping situations. These include unusual shopping behaviors that can create a long and complicated sequence of activities requiring additional effort to analyze what happened.

Previous generations of the Just Walk Out system utilized a modular architecture; it tackled complex shopping situations by breaking down the shopper’s visit into discrete tasks, such as detecting shopper interactions, tracking items, identifying products, and counting what is selected. These individual components were then integrated into sequential pipelines to enable the overall system functionality. While this approach produced highly accurate receipts, significant engineering efforts are required to address challenges in new, previously unencountered situations and complex shopping scenarios. This limitation restricted the scalability of this approach.

The solution: Just Walk Out multi-modal AI

To meet these challenges, we introduced a new multi-modal FM that we designed specifically for retail store environments, enabling Just Walk Out technology to handle complex real-world shopping scenarios. The new multi-modal FM further enhances the Just Walk Out system’s capabilities by generalizing more effectively to new store formats, products, and customer behaviors, which is crucial for scaling up Just Walk Out technology.

The incorporation of continuous learning enables the model training to automatically adapt and learn from new challenging scenarios as they arise. This self-improving capability helps ensure the system maintains high performance, even as shopping environments continue to evolve.

Through this combination of end-to-end learning and enhanced generalization, the Just Walk Out system can tackle a wider range of dynamic and complex retail settings. Retailers can confidently deploy this technology, knowing it will provide a frictionless checkout-free experience for their customers.

The following video shows our system’s architecture in action.

Key elements of our Just Walk Out multi-modal AI model include:

• Flexible data inputs –The system tracks how users interact with products and fixtures, such as shelves or fridges. It primarily relies on multi-view video feeds as inputs, using weight sensors solely to track small items. The model maintains a digital 3D representation of the store and can access catalog images to identify products, even if the shopper returns items to the shelf incorrectly.
• Multi-modal AI tokens to represent shoppers’ journeys – The multi-modal data inputs are processed by the encoders, which compress them into transformer tokens, the basic unit of input for the receipt model. This allows the model to interpret hand movements, differentiate between items, and accurately count the number of items picked up or returned to the shelf with speed and precision.
• Continuously updating receipts – The system uses tokens to create digital receipts for each shopper. It can differentiate between different shopper sessions and dynamically updates each receipt as they pick up or return items.

Training the Just Walk Out FM

By feeding vast amounts of multi-modal data into the Just Walk Out FM, we found it could consistently generate—or, technically, “predict”— accurate receipts for shoppers. To improve accuracy, we designed over 10 auxiliary tasks, such as detection, tracking, image segmentation, grounding (linking abstract concepts to real-world objects), and activity recognition. All of these are learned within a single model, enhancing the model’s ability to handle new, never-before-seen store formats, products, and customer behaviors. This is crucial for bringing Just Walk Out technology to new locations.

AI model training—in which curated data is fed to selected algorithms—helps the system refine itself to produce accurate results. We quickly discovered we could accelerate the training of our model by using a data flywheel that continuously mines and labels high-quality data in a self-reinforcing cycle. The system is designed to integrate these progressive improvements with minimal manual intervention. The following diagram illustrates the process.

To train an FM effectively, we invested in a robust infrastructure that can efficiently process the massive amounts of data needed to train high-capacity neural networks that mimic human decision-making. We built the infrastructure for our Just Walk Out model with the help of several Amazon Web Services (AWS) services, including Amazon Simple Storage Service (Amazon S3) for data storage and Amazon SageMaker for training.

To train an FM effectively, we invested in a robust infrastructure that can efficiently process the massive amounts of data needed to train high-capacity neural networks that mimic human decision-making. We built the infrastructure for our Just Walk Out model with the help of several Amazon Web Services (AWS) services, including Amazon Simple Storage Service (Amazon S3) for data storage and Amazon SageMaker for training.

Here are some key steps we followed in training our FM:

• Selecting challenging data sources – To train our AI model for Just Walk Out technology, we focus on training data from especially difficult shopping scenarios that test the limits of our model. Although these complex cases constitute only a small fraction of shopping data, they are the most valuable for helping the model learn from its mistakes.
• Leveraging auto labeling – To increase operational efficiency, we developed algorithms and models that automatically attach meaningful labels to the data. In addition to receipt prediction, our automated labeling algorithms cover the auxiliary tasks, ensuring the model gains comprehensive multi-modal understanding and reasoning capabilities.
• Pre-training the model – Our FM is pre-trained on a vast collection of multi-modal data across a diverse range of tasks, which enhances the model’s ability to generalize to new store environments never encountered before.
• Fine-tuning the model – Finally, we refined the model further and used quantization techniques to create a smaller, more efficient model that uses edge computing.

As the data flywheel continues to operate, it will progressively identify and incorporate more high-quality, challenging cases to test the robustness of the model. These additional difficult samples are then fed into the training set, further enhancing the model’s accuracy and applicability across new physical store environments.

Conclusion

In this post, we showed how our multi-modal, AI system represents significant new possibilities for Just Walk Out technology. With our innovative approach, we are moving away from modular AI systems that rely on human-defined subcomponents and interfaces. Instead, we’re building simpler and more scalable AI systems that can be trained end-to-end. Although we’ve just scratched the surface, multi-modal AI has raised the bar for our already highly accurate receipt system and will enable us to improve the shopping experience at more Just Walk Out technology stores around the world.

Visit About Amazon to read the official announcement about the new multi-modal AI system and learn more about the latest improvements in Just Walk Out technology.

To find where you can find Just Walk Out technology locations, visit Just Walk Out technology locations near you. Learn more about how to power your store or venue with Just Walk Out technology by Amazon on the Just Walk Out technology product page.

Visit Build and scale the next wave of AI innovation on AWS to learn more about how AWS can reinvent customer experiences with the most comprehensive set of AI and ML services.

About the Authors

Tian Lan is a Principal Scientist at AWS. He currently leads the research efforts in developing the next-generation Just Walk Out 2.0 technology, transforming it into an end-to-end learned, store domain–focused multi-modal foundation model.

Chris Broaddus is a Senior Manager at AWS. He currently manages all the research efforts for Just Walk Out technology, including the multi-modal AI model and other projects, such as deep learning for human pose estimation and Radio Frequency Identification (RFID) receipt prediction.

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Evaluating your Retrieval Augmented Generation (RAG) system to make sure it fulfils your business requirements is paramount before deploying it to production environments. However, this requires acquiring a high-quality dataset of real-world question-answer pairs, which can be a daunting task, especially in the early stages of development. This is where synthetic data generation comes into play. With Amazon Bedrock, you can generate synthetic datasets that mimic actual user queries, enabling you to evaluate your RAG system’s performance efficiently and at scale. With synthetic data, you can streamline the evaluation process and gain confidence in your system’s capabilities before unleashing it to the real world.

This post explains how to use Anthropic Claude on Amazon Bedrock to generate synthetic data for evaluating your RAG system. Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon through a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.

Fundamentals of RAG evaluation

Before diving deep into how to evaluate a RAG application, let’s recap the basic building blocks of a naive RAG workflow, as shown in the following diagram.

The workflow consists of the following steps:

1. In the ingestion step, which happens asynchronously, data is split into separate chunks. An embedding model is used to generate embeddings for each of the chunks, which are stored in a vector store.
2. When the user asks a question to the system, an embedding is generated from the questions and the top-k most relevant chunks are retrieved from the vector store.
3. The RAG model augments the user input by adding the relevant retrieved data in context. This step uses prompt engineering techniques to communicate effectively with the large language model (LLM). The augmented prompt allows the LLM to generate an accurate answer to user queries.
4. An LLM is prompted to formulate a helpful answer based on the user’s questions and the retrieved chunks.

Amazon Bedrock Knowledge Bases offers a streamlined approach to implement RAG on AWS, providing a fully managed solution for connecting FMs to custom data sources. To implement RAG using Amazon Bedrock Knowledge Bases, you begin by specifying the location of your data, typically in Amazon Simple Storage Service (Amazon S3), and selecting an embedding model to convert the data into vector embeddings. Amazon Bedrock then creates and manages a vector store in your account, typically using Amazon OpenSearch Serverless, handling the entire RAG workflow, including embedding creation, storage, management, and updates. You can use the RetrieveAndGenerate API for a straightforward implementation, which automatically retrieves relevant information from your knowledge base and generates responses using a specified FM. For more granular control, the Retrieve API is available, allowing you to build custom workflows by processing retrieved text chunks and developing your own orchestration for text generation. Additionally, Amazon Bedrock Knowledge Bases offers customization options, such as defining chunking strategies and selecting custom vector stores like Pinecone or Redis Enterprise Cloud.

A RAG application has many moving parts, and on your way to production you’ll need to make changes to various components of your system. Without a proper automated evaluation workflow, you won’t be able to measure the effect of these changes and will be operating blindly regarding the overall performance of your application.

To evaluate such a system properly, you need to collect an evaluation dataset of typical user questions and answers.

Moreover, you need to make sure you evaluate not only the generation part of the process but also the retrieval. An LLM without relevant retrieved context can’t answer the user’s question if the information wasn’t present in the training data. This holds true even if it has exceptional generation capabilities.

As such, a typical RAG evaluation dataset consists of the following minimum components:

• A list of questions users will ask the RAG system
• A list of corresponding answers to evaluate the generation step
• The context or a list of contexts that contain the answer for each question to evaluate the retrieval

In an ideal world, you would take real user questions as a basis for evaluation. Although this is the optimal approach because it directly resembles end-user behavior, this is not always feasible, especially in the early stages of building a RAG system. As you progress, you should aim for incorporating real user questions into your evaluation set.

To learn more about how to evaluate a RAG application, see Evaluate the reliability of Retrieval Augmented Generation applications using Amazon Bedrock.

Solution overview

We use a sample use case to illustrate the process by building an Amazon shareholder letter chatbot that allows business analysts to gain insights about the company’s strategy and performance over the past years.

For the use case, we use PDF files of Amazon’s shareholder letters as our knowledge base. These letters contain valuable information about the company’s operations, initiatives, and future plans. In a RAG implementation, the knowledge retriever might use a database that supports vector searches to dynamically look up relevant documents that serve as the knowledge source.

The following diagram illustrates the workflow to generate the synthetic dataset for our RAG system.

The workflow includes the following steps:

1. Load the data from your data source.
2. Chunk the data as you would for your RAG application.
3. Generate relevant questions from each document.
4. Generate an answer by prompting an LLM.
5. Extract the relevant text that answers the question.
6. Evolve the question according to a specific style.
7. Filter questions and improve the dataset either using domain experts or LLMs using critique agents.

We use a model from the Anthropic’s Claude 3 model family to extract questions and answers from our knowledge source, but you can experiment with other LLMs as well. Amazon Bedrock makes this effortless by providing standardized API access to many FMs.

For the orchestration and automation steps in this process, we use LangChain. LangChain is an open source Python library designed to build applications with LLMs. It provides a modular and flexible framework for combining LLMs with other components, such as knowledge bases, retrieval systems, and other AI tools, to create powerful and customizable applications.

The next sections walk you through the most important parts of the process. If you want to dive deeper and run it yourself, refer to the notebook on GitHub.

Load and prepare the data

First, load the shareholder letters using LangChain’s PyPDFDirectoryLoader and use the RecursiveCharacterTextSplitter to split the PDF documents into chunks. The RecursiveCharacterTextSplitter divides the text into chunks of a specified size while trying to preserve context and meaning of the content. It’s a good way to start when working with text-based documents. You don’t have to split your documents to create your evaluation dataset if your LLM supports a context window that is large enough to fit your documents, but you could potentially end up with a lower quality of generated questions due to the larger size of the task. You want to have the LLM generate multiple questions per document in this case.

from langchain.text_splitter import CharacterTextSplitter, RecursiveCharacterTextSplitter
from langchain.document_loaders.pdf import PyPDFLoader, PyPDFDirectoryLoader

# Load PDF documents from directory
loader = PyPDFDirectoryLoader("./synthetic_dataset_generation/")  
documents = loader.load()
# Use recursive character splitter, works better for this PDF data set
text_splitter = RecursiveCharacterTextSplitter(
    # Split documents into small chunks
    chunk_size = 1500,  
    # Overlap chunks to reduce cutting sentences in half
    chunk_overlap  = 100,
    separators=["nn", "n", ".", " ", ""],
)

# Split loaded documents into chunks
docs = text_splitter.split_documents(documents)

To demonstrate the process of generating a corresponding question and answer and iteratively refining them, we use an example chunk from the loaded shareholder letters throughout this post:

“page_content=''Our AWS and Consumer businesses have had different demand trajectories during the pandemic. In thenfirst year of the pandemic, AWS revenue continued to grow at a rapid clip—30% year over year (“Y oY”) in2020 on a $35 billion annual revenue base in 2019—but slower than the 37% Y oY growth in 2019. [...] This shift by so many companies (along with the economy recovering) helped re-accelerate AWS’s revenue growth to 37% Y oY in 2021.nConversely, our Consumer revenue grew dramatically in 2020. In 2020, Amazon’s North America andnInternational Consumer revenue grew 39% Y oY on the very large 2019 revenue base of $245 billion; and,this extraordinary growth extended into 2021 with revenue increasing 43% Y oY in Q1 2021. These areastounding numbers. We realized the equivalent of three years’ forecasted growth in about 15 months.nAs the world opened up again starting in late Q2 2021, and more people ventured out to eat, shop, and travel,”

Generate an initial question

To facilitate prompting the LLM using Amazon Bedrock and LangChain, you first configure the inference parameters. To accurately extract more extensive contexts, set the max_tokens parameter to 4096, which corresponds to the maximum number of tokens the LLM will generate in its output. Additionally, define the temperature parameter as 0.2 because the goal is to generate responses that adhere to the specified rules while still allowing for a degree of creativity. This value differs for different use cases and can be determined by experimentation.

import boto3

from langchain_community.chat_models import BedrockChat

# set up a Bedrock-runtime client for inferencing large language models
boto3_bedrock = boto3.client('bedrock-runtime')
# Choosing claude 3 Haiku due to cost and performance efficiency
claude_3_haiku = "anthropic.claude-3-haiku-20240307-v1:0"
# Set-up langchain LLM for implementing the synthetic dataset generation logic

# for each model provider there are different parameters to define when inferencing against the model
inference_modifier = {
                        "max_tokens": 4096,
                        "temperature": 0.2
                    }
                                         
llm = BedrockChat(model_id = claude_3_haiku,
                    client = boto3_bedrock, 
                    model_kwargs = inference_modifier 
                    )

You use each generated chunk to create synthetic questions that mimic those a real user might ask. By prompting the LLM to analyze a portion of the shareholder letter data, you generate relevant questions based on the information presented in the context. We use the following sample prompt to generate a single question for a specific context. For simplicity, the prompt is hardcoded to generate a single question, but you can also instruct the LLM to generate multiple questions with a single prompt.

The rules can be adapted to better guide the LLM in generating questions that reflect the types of queries your users would pose, tailoring the approach to your specific use case.

# Create a prompt template to generate a question a end-user could have about a given context
initial_question_prompt_template = PromptTemplate(
    input_variables=["context"],
    template="""
    <Instructions>
    Here is some context:
    <context>
    {context}
    </context>

    Your task is to generate 1 question that can be answered using the provided context, following these rules:

    <rules>
    1. The question should make sense to humans even when read without the given context.
    2. The question should be fully answered from the given context.
    3. The question should be framed from a part of context that contains important information. It can also be from tables, code, etc.
    4. The answer to the question should not contain any links.
    5. The question should be of moderate difficulty.
    6. The question must be reasonable and must be understood and responded by humans.
    7. Do not use phrases like 'provided context', etc. in the question.
    8. Avoid framing questions using the word "and" that can be decomposed into more than one question.
    9. The question should not contain more than 10 words, make use of abbreviations wherever possible.
    </rules>

    To generate the question, first identify the most important or relevant part of the context. Then frame a question around that part that satisfies all the rules above.

    Output only the generated question with a "?" at the end, no other text or characters.
    </Instructions>
    
    """)

The following is the generated question from our example chunk:

What is the price-performance improvement of AWS Graviton2 chip over x86 processors?

Generate answers

To use the questions for evaluation, you need to generate a reference answer for each of the questions to test against. With the following prompt template, you can generate a reference answer to the created question based on the question and the original source chunk:

# Create a prompt template that takes into consideration the the question and generates an answer
answer_prompt_template = PromptTemplate(
    input_variables=["context", "question"],
    template="""
    <Instructions>
    <Task>
    <role>You are an experienced QA Engineer for building large language model applications.</role>
    <task>It is your task to generate an answer to the following question <question>{question}</question> only based on the <context>{context}</context></task>
    The output should be only the answer generated from the context.

    <rules>
    1. Only use the given context as a source for generating the answer.
    2. Be as precise as possible with answering the question.
    3. Be concise in answering the question and only answer the question at hand rather than adding extra information.
    </rules>

    Only output the generated answer as a sentence. No extra characters.
    </Task>
    </Instructions>
    
    Assistant:""")

The following is the generated answer based on the example chunk:

“The AWS revenue grew 37% year-over-year in 2021.”

Extract relevant context

To make the dataset verifiable, we use the following prompt to extract the relevant sentences from the given context to answer the generated question. Knowing the relevant sentences, you can check whether the question and answer are correct.

# To check whether an answer was correctly formulated by the large language model you get the relevant text passages from the documents used for answering the questions.
source_prompt_template = PromptTemplate(
    input_variables=["context", "question"],
    template="""Human:
    <Instructions>
    Here is the context:
    <context>
    {context}
    </context>

    Your task is to extract the relevant sentences from the given context that can potentially help answer the following question. You are not allowed to make any changes to the sentences from the context.

    <question>
    {question}
    </question>

    Output only the relevant sentences you found, one sentence per line, without any extra characters or explanations.
    </Instructions>
    Assistant:""")

The following is the relevant source sentence extracted using the preceding prompt:

“This shift by so many companies (along with the economy recovering) helped re-accelerate AWS's revenue growth to 37% Y oY in 2021.”

Refine questions

When generating question and answer pairs from the same prompt for the whole dataset, it might appear that the questions are repetitive and similar in form, and therefore don’t mimic real end-user behavior. To prevent this, take the previously created questions and prompt the LLM to modify them according to the rules and guidance established in the prompt. By doing so, a more diverse dataset is synthetically generated. The prompt for generating questions tailored to your specific use case heavily depends on that particular use case. Therefore, your prompt must accurately reflect your end-users by setting appropriate rules or providing relevant examples. The process of refining questions can be repeated as many times as necessary.

# To generate a more versatile testing dataset you alternate the questions to see how your RAG systems performs against differently formulated of questions
question_compress_prompt_template = PromptTemplate(
    input_variables=["question"],
    template="""
    <Instructions>
    <role>You are an experienced linguistics expert for building testsets for large language model applications.</role>

    <task>It is your task to rewrite the following question in a more indirect and compressed form, following these rules:

    <rules>
    1. Make the question more indirect
    2. Make the question shorter
    3. Use abbreviations if possible
    </rules>

    <question>
    {question}
    </question>

    Your output should only be the rewritten question with a question mark "?" at the end. Do not provide any other explanation or text.
    </task>
    </Instructions>
    
    """)

Users of your application might not always use your solution in the same way, for instance using abbreviations when asking questions. This is why it’s crucial to develop a diverse dataset:

“AWS rev YoY growth in ’21?”

Automate dataset generation

To scale the process of the dataset generation, we iterate over all the chunks in our knowledge base; generate questions, answers, relevant sentences, and refinements for each question; and save them to a pandas data frame to prepare the full dataset.

To provide a clearer understanding of the structure of the dataset, the following table presents a sample row based on the example chunk used throughout this post.

On average, the generation of questions with a given context of 1,500–2,000 tokens results in an average processing time of 2.6 seconds for a set of initial question, answer, evolved question, and source sentence discovery using Anthropic Claude 3 Haiku. The generation of 1,000 sets of questions and answers costs approximately $2.80 USD using Anthropic Claude 3 Haiku. The pricing page gives a detailed overview of the cost structure. This results in a more time- and cost-efficient generation of datasets for RAG evaluation compared to the manual generation of these questions sets.

Improve your dataset using critique agents

Although using synthetic data is a good starting point, the next step should be to review and refine the dataset, filtering out or modifying questions that aren’t relevant to your specific use case. One effective approach to accomplish this is by using critique agents.

Critique agents are a technique used in natural language processing (NLP) to evaluate the quality and suitability of questions in a dataset for a particular task or application using a machine learning model. In our case, the critique agents are employed to assess whether the questions in the dataset are valid and appropriate for our RAG system.

The two main metrics evaluated by the critique agents in our example are question relevance and answer groundedness. Question relevance determines how relevant the generated question is for a potential user of our system, and groundedness assesses whether the generated answers are indeed based on the given context.

groundedness_check_prompt_template = PromptTemplate(
    input_variables=["context","question"],
    template="""
    <Instructions>
    You will be given a context and a question related to that context.

    Your task is to provide an evaluation of how well the given question can be answered using only the information provided in the context. Rate this on a scale from 1 to 5, where:

    1 = The question cannot be answered at all based on the given context
    2 = The context provides very little relevant information to answer the question
    3 = The context provides some relevant information to partially answer the question 
    4 = The context provides substantial information to answer most aspects of the question
    5 = The context provides all the information needed to fully and unambiguously answer the question

    First, read through the provided context carefully:

    <context>
    {context}
    </context>

    Then read the question:

    <question>
    {question}
    </question>

    Evaluate how well you think the question can be answered using only the context information. Provide your reasoning first in an <evaluation> section, explaining what relevant or missing information from the context led you to your evaluation score in only one sentence.

    Provide your evaluation in the following format:

    <rating>(Your rating from 1 to 5)</rating>
    
    <evaluation>(Your evaluation and reasoning for the rating)</evaluation>


    </Instructions>
    
    """)

relevance_check_prompt_template = PromptTemplate(
    input_variables=["question"],
    template="""
    <Instructions>
    You will be given a question related to Amazon Shareholder letters. Your task is to evaluate how useful this question would be for a financial and business analyst working in wallstreet.

    To evaluate the usefulness of the question, consider the following criteria:

    1. Relevance: Is the question directly relevant to your work? Questions that are too broad or unrelated to this domain should receive a lower rating.

    2. Practicality: Does the question address a practical problem or use case that analysts might encounter? Theoretical or overly academic questions may be less useful.

    3. Clarity: Is the question clear and well-defined? Ambiguous or vague questions are less useful.

    4. Depth: Does the question require a substantive answer that demonstrates understanding of financial topics? Surface-level questions may be less useful.

    5. Applicability: Would answering this question provide insights or knowledge that could be applied to real-world company evaluation tasks? Questions with limited applicability should receive a lower rating.

    Provide your evaluation in the following format:

    <rating>(Your rating from 1 to 5)</rating>
    
    <evaluation>(Your evaluation and reasoning for the rating)</evaluation>

    Here is the question:

    {question}
    </Instructions>
    """)

Evaluating the generated questions helps with assessing the quality of a dataset and eventually the quality of the evaluation. The generated question was rated very well:

Groundedness score: 5
“The context provides the exact information needed to answer the question[...]”

Relevance score: 5
“This question is highly relevant and useful for a financial and business analyst working on Wall Street. AWS (Amazon Web Services) is a key business segment for Amazon, and understanding its year-over-year (YoY) revenue growth is crucial for evaluating the company's overall performance and growth trajectory.[...].

Best practices for generating synthetic datasets

Although generating synthetic datasets offers numerous benefits, it’s essential to follow best practices to maintain the quality and representativeness of the generated data:

• Combine with real-world data – Although synthetic datasets can mimic real-world scenarios, they might not fully capture the nuances and complexities of actual human interactions or edge cases. Combining synthetic data with real-world data can help address this limitation and create more robust datasets.
• Choose the right model – Choose different LLMs for dataset creation than used for RAG generation in order to avoid self-enhancement bias.
• Implement robust quality assurance – You can employ multiple quality assurance mechanisms, such as critique agents, human evaluation, and automated checks, to make sure the generated datasets meet the desired quality standards and accurately represent the target use case.
• Iterate and refine – You should treat synthetic dataset generation as an iterative process. Continuously refine and improve the process based on feedback and performance metrics, adjusting parameters, prompts, and quality assurance mechanisms as needed.
• Domain-specific customization – For highly specialized or niche domains, consider fine-tuning the LLM (such as with PEFT or RLHF) by injecting domain-specific knowledge to improve the quality and accuracy of the generated datasets.

Conclusion

The generation of synthetic datasets is a powerful technique that can significantly enhance the evaluation process of your RAG system, especially in the early stages of development when real-world data is scarce or difficult to obtain. By taking advantage of the capabilities of LLMs, this approach enables the creation of diverse and representative datasets that accurately mimic real human interactions, while also providing the scalability necessary to meet your evaluation needs.

Although this approach offers numerous benefits, it’s essential to acknowledge its limitations. Firstly, the quality of the synthetic dataset heavily relies on the performance and capabilities of the underlying language model, knowledge retrieval system, and quality of prompts used for generation. Being able to understand and adjust the prompts for generation is crucial in this process. Biases and limitations present in these components may be reflected in the generated dataset. Additionally, capturing the full complexity and nuances of real-world interactions can be challenging because synthetic datasets may not account for all edge cases or unexpected scenarios.

Despite these limitations, generating synthetic datasets remains a valuable tool for accelerating the development and evaluation of RAG systems. By streamlining the evaluation process and enabling iterative development cycles, this approach can contribute to the creation of better-performing AI systems.

We encourage developers, researchers, and enthusiasts to explore the techniques mentioned in this post and the accompanying GitHub repository and experiment with generating synthetic datasets for your own RAG applications. Hands-on experience with this technique can provide valuable insights and contribute to the advancement of RAG systems in various domains.

About the Authors

Johannes Langer is a Senior Solutions Architect at AWS, working with enterprise customers in Germany. Johannes is passionate about applying machine learning to solve real business problems. In his personal life, Johannes enjoys working on home improvement projects and spending time outdoors with his family.

Lukas Wenzel is a Solutions Architect at Amazon Web Services in Hamburg, Germany. He focuses on supporting software companies building SaaS architectures. In addition to that, he engages with AWS customers on building scalable and cost-efficient generative AI features and applications. In his free-time, he enjoys playing basketball and running.

David Boldt is a Solutions Architect at Amazon Web Services. He helps customers build secure and scalable solutions that meet their business needs. He is specialized in machine learning to address industry-wide challenges, using technologies to drive innovation and efficiency across various sectors.

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State and local agencies spend approximately $1.23 billion annually to operate and maintain signalized traffic intersections. On the other end, traffic congestion at intersections costs drivers about $22 billion annually. Implementing an artificial intelligence (AI)-powered detection-based solution can significantly mitigate congestion at intersections and reduce operation and maintenance costs. In this blog post, we show you how Amazon Rekognition (an AI technology) can mitigate congestion at traffic intersections and reduce operations and maintenance costs.

State and local agencies rely on traffic signals to facilitate the safe flow of traffic involving cars, pedestrians, and other users. There are two main types of traffic lights: fixed and dynamic. Fixed traffic lights are timed lights controlled by electro-mechanical signals that switch and hold the lights based on a set period of time. Dynamic traffic lights are designed to adjust based on traffic conditions by using detectors both underneath the surface of the road and above the traffic light. However, as population continues to rise, there are more cars, bikes, and pedestrians using the streets. This increase in road users can negatively impact the efficiency of either of the two traffic systems.

Solution overview

At a high level, our solution uses Amazon Rekognition to automatically detect objects (cars, bikes, and so on) and scenes at an intersection. After detection, Amazon Rekognition creates bounding boxes around each object (such as a vehicle) and calculates the distance between each object (in this scenario, that would be the distance between vehicles detected at an intersection). Results from the calculated distances are used programmatically to stop or allow the flow of traffic, thus reducing congestion. All of this happens without human intervention.

Prerequisties

The proposed solution can be implemented in a personal AWS environment using the code that we provide. However, there are a few prerequisites that must in place. Before running the labs in this post, ensure you have the following:

1. An AWS account. Create one if necessary.
2. The appropriate AWS Identity and Access Management (IAM) permissions to access services used in the lab. If this is your first time setting up an AWS account, see the IAM documentation for information about configuring IAM.
3. A SageMaker Studio Notebook. Create one if necessary.

Solution architecture

The following diagram illustrates the lab’s architecture:

This solution uses the following AI and machine learning (AI/ML), serverless, and managed technologies:

• Amazon SageMaker, a fully managed machine learning service that enables data scientists and developers to build, train and deploy machine learning applications.
• Amazon Rekognition supports adding image and video analysis to your applications.
• IAM grants authentication and authorization that allows resources in the solution to talk to each other.

To recap how the solution works

1. Traffic intersection video footage is uploaded to your SageMaker environment from an external device.
2. A Python function uses CV2 to split the video footage into image frames.
3. The function makes a call to Amazon Rekognition when the image frames are completed.
4. Amazon Rekognition analyzes each frame and creates bounding boxes around each vehicle it detects.
5. The function counts the bounding boxes and changes the traffic signal based on the number of cars it detects using pre-defined logic.

Solution walkthrough

Now, let’s walk through implementing the solution.

Configure SageMaker:

1. Choose Domains in the navigation pane, and then select your domain name.
2. Find and copy the SageMaker Execution Role.
   
3. Go to the IAM console and choose Roles in the navigation pane and paste the SageMaker Execution Role you copied in the preceding step.

Enable SageMaker to interact with Amazon Rekognition:

Next, enable SageMaker to interact with Amazon Rekognition using the SageMaker execution role.

1. In the SageMaker console, select your SageMaker execution role and choose Add permission and then choose Attach policies.
2. In the search bar, enter and select AmazonRekognitionFullAccess Policy. See the following figure.

With the IAM permissions configured, you can run the notebook in SageMaker with access to Amazon Rekognition for the video analysis.

Download the Rekognition Notebook and traffic intersection data to your local environment. On the Amazon Sagemaker Studio, upload the notebook and data you downloaded.

Code walkthrough:

This lab uses OpenCv and Boto3 to prepare the SageMaker environment. OpenCv is an open source library with over 250 algorithms for computer vision analysis. Boto3 is the AWS SDK for Python that helps you to integrate AWS services with applications or scripts written in Python.

1. First, we import OpenCv and Boto3 package. The next cell of codes builds a function for analyzing the video. We will walk through key components of the function. The function starts by creating a frame for the video to be analyzed.
   
2. The frame is written to a new video writer file with an MP4 extension. The function also loops through the file and, if the video doesn’t have a frame, the function converts it to a JPEG file. Then the code define and identify traffic lanes using bounding boxes. Amazon Rekognition image operations place bounding boxes around images detected for later analysis.
   
3. The function captures the video frame and sends it to Amazon Rekognition to analyze images in the video using the bounding boxes. The model uses bounding boxes to detect and classify captured images (cars, pedestrians, and so on) in the video. The code then detects whether a car is in the video sent to Amazon Rekognition. A bounding box is generated for each car detected in the video.
   
4. The size and position of the car is computed to accurately detect its position. After computing the size and position of the car, the model checks whether the car is in a detected lane. After determining whether there are cars in one of the detected lanes, the model counts the numbers of detected cars in the lane.
   
5. The results from detecting and computing the size, position and numbers of cars in a lane are written to a new file in the rest of the function.
   
6. Writing the outputs to a new file, a few geometry computations are done to determine the details of detected objects. For example, polygons are used to determine the size of objects.
   
7. With the function completely built, the next step is running the function and with a minimum confidence sore of 95% using a test video.
   
8. The last line of codes allow you to download the video from the directory in SageMaker to check the results and confidence level of the output.

Costs

The logic behind our cost estimates is put at $6,000 per intersection with the assumption one frame per second using four cameras with a single SageMaker notebook for each intersection. One important callout is that not every intersection is a 4-way intersection. Implementing this solution on more populated traffic areas will increase the overall flow of traffic.

Cost breakdown and details

However, this is an estimate, and you may incur additional costs depending on customization. For additional information on costs, visit the AWS pricing page for the services covered in the solution architecture. If you have questions, reach out to the AWS team for a more technical and focused discussion.

Clean up

Delete all AWS resources created for this solution that are no longer needed to avoid future charges.

Conclusion

This post provides a solution to make traffic lights more efficient using Amazon Rekognition. The solution proposed in this post can mitigate costs, support road safety, and reduce congestion at intersections. All of these make traffic management more efficient. We strongly recommend learning more about how Amazon Rekognition can help accelerate other image recognition and video analysis tasks by visiting the Amazon Rekognition Developer Guide.

About the authors

Hao Lun Colin Chu is an innovative Solution Architect at AWS, helping partners and customers leverage cutting-edge cloud technologies to solve complex business challenges. With extensive expertise in cloud migrations, modernization, and AI/ML, Colin advises organizations on translating their needs into transformative AWS-powered solutions. Driven by a passion for using technology as a force for good, he is committed to delivering solutions that empower organizations and improve people’s lives. Outside of work, he enjoys playing drum, volleyball and board games!

Joe Wilson is a Solutions Architect at Amazon Web Services supporting nonprofit organizations. He provides technical guidance to nonprofit organizations seeking to securely build, deploy or expand applications in the cloud. He is passionate about leveraging data and technology for social good. Joe background is in data science and international development. Outside work, Joe loves spending time with his family, friends and chatting about innovation and entrepreneurship.

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Machine learning (ML) projects are inherently complex, involving multiple intricate steps—from data collection and preprocessing to model building, deployment, and maintenance. Data scientists face numerous challenges throughout this process, such as selecting appropriate tools, needing step-by-step instructions with code samples, and troubleshooting errors and issues. These iterative challenges can hinder progress and slow down projects. Fortunately, generative AI-powered developer assistants like Amazon Q Developer have emerged to help data scientists streamline their workflows and fast-track ML projects, allowing them to save time and focus on strategic initiatives and innovation.

Amazon Q Developer is fully integrated with Amazon SageMaker Studio, an integrated development environment (IDE) that provides a single web-based interface for managing all stages of ML development. You can use this natural language assistant from your SageMaker Studio notebook to get personalized assistance using natural language. It offers tool recommendations, step-by-step guidance, code generation, and troubleshooting support. This integration simplifies your ML workflow and helps you efficiently build, train, and deploy ML models without needing to leave SageMaker Studio to search for additional resources or documentation.

In this post, we present a real-world use case analyzing the Diabetes 130-US hospitals dataset to develop an ML model that predicts the likelihood of readmission after discharge. Throughout this exercise, you use Amazon Q Developer in SageMaker Studio for various stages of the development lifecycle and experience firsthand how this natural language assistant can help even the most experienced data scientists or ML engineers streamline the development process and accelerate time-to-value.

Solution overview

If you’re an AWS Identity and Access Management (IAM) and AWS IAM Identity Center user, you can use your Amazon Q Developer Pro tier subscription within Amazon SageMaker. Administrators can subscribe users to the Pro Tier on the Amazon Q Developer console, enable Pro Tier in the SageMaker domain settings, and provide the Amazon Q Developer profile Amazon Resource Name (ARN). The Pro Tier offers unlimited chat and inline code suggestions. Refer to Set up Amazon Q Developer for your users for detailed instructions.

If you don’t have a Pro Tier subscription but want to try out the capability, you can access the Amazon Q Developer Free Tier by adding the relevant policies to your SageMaker service roles. Admins can navigate to the IAM console, search for the SageMaker Studio role, and add the policy outlined in Set up Amazon Q Developer for your users. The Free Tier is available for both IAM and IAM Identity Center users.

To start our ML project predicting the probability of readmission for diabetes patients, you need to download the Diabetes 130-US hospitals dataset. This dataset contains 10 years (1999–2008) of clinical care data at 130 US hospitals and integrated delivery networks. Each row represents hospital records of patients diagnosed with diabetes, who underwent laboratory, and more.

At the time of writing, Amazon Q Developer support in SageMaker Studio is only available in JupyterLab spaces. Amazon Q Developer is not supported for shared spaces.

Amazon Q Developer chat

After you have uploaded the data to SageMaker Studio, you can start working on your ML problem of reducing readmission rates for diabetes patients. Begin by using the chat capability next to your JupyterLab notebook. You can ask questions like generating code to parse the Diabetes 130-US hospitals data, how you should formulate this ML problem, and develop a plan to build an ML model that predicts the likelihood of readmission after discharge. Amazon Q Developer uses AI to provide code recommendations, and this is non-deterministic. The results you get may be different from the ones shown in the following screenshot.

You can ask Amazon Q Developer to help you plan out the ML project. In this case, we want the assistant to show us how to train a random forest classifier using the Diabetes 130-US dataset. Enter the following prompt into the chat, and Amazon Q Developer will generate a plan. If code is generated, you can use the UI to directly insert the code into your notebook.

I have diabetic_data.csv file containing training data about whether a diabetic patient was readmitted after discharge. I want to use this data to train a random forest classifier using scikit-learn. Can you list out the steps to build this model?

You can ask Amazon Q Developer to help you generate code for specific tasks by inserting the following prompt:

Create a function that takes in a pandas DataFrame and performs one-hot encoding for the gender, race, A1Cresult, and max_glu_serum columns.

You can also ask Amazon Q Developer to explain existing code and troubleshoot for common errors. Just choose the cell with the error and enter /fix in the chat.

The following is a full list of the shortcut commands:

• /help – Display this help message
• /fix – Fix an error cell selected in your notebook
• /clear – Clear the chat window
• /export – Export chat history to a Markdown file

To get the most out of your Amazon Q Developer chat, the following best practices are recommended when crafting your prompt:

• Be direct and specific – Ask precise questions. For instance, instead of a vague query about AWS services, try: “Can you provide sample code using the SageMaker Python SDK library to train an XGBoost model in SageMaker?” Specificity helps the assistant understand exactly what you need, resulting in more accurate and useful responses.
• Provide contextual information – The more context you offer, the better. This allows Amazon Q Developer to tailor its responses to your specific situation. For example, don’t just ask for code to prepare data. Instead, provide the first three rows of your data to get better code suggestions with fewer changes needed.
• Avoid sensitive topics – Amazon Q Developer is designed with guardrail controls. It’s best to avoid questions related to security, billing information of your account, or other sensitive subjects.

Following these guidelines can help you maximize the value of Amazon Q Developer’s AI-powered code recommendations and streamline your ML projects.

Amazon Q Developer inline code suggestions

You can also get real-time code suggestions as you type in the JupyterLab notebook, offering context-aware recommendations based on your existing code and comments to streamline the coding process. In the following example, we demonstrate how to use the inline code suggestions feature to generate code blocks for various data science tasks: from data exploration to feature engineering, training a random forest model, evaluating the model, and finally deploying the model to predict the probability of readmission for diabetes patients.

The following figure shows the list of keyboard shortcuts to interact with Amazon Q Developer.

Let’s start with data exploration.

We first import some of the necessary Python libraries, like pandas and NumPy. Add the following code into the first code cell of Jupyter Notebook, and then run the cell:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

In the next code cell, add the following comment, and before running the cell, press Enter and Tab. You can watch the bottom status bar to see Amazon Q Developer working to generate code suggestions.

# read 'diabetic-readmission.csv'

You can also ask Amazon Q Developer to create a visualization:

# create a bar chart from df that shows counts of patients by 'race' and 'gender' with a title of 'patients by race and gender' 

Now you can perform feature engineering to prepare the model for training.

The dataset provided has a number of categorical features, which need to be converted to numerical features, as well as missing data. In the next code cell, add the following comment, and press TAB to see how Amazon Q Developer can help:

# perform one-hot encoding for gender, race, a1c_result, and max_glu_serum columns 

Lastly, you can use Amazon Q Developer to help you create a simple ML model, random forest classifier, using scikit-learn.

Amazon Q Developer in SageMaker data policy

When using Amazon Q Developer in SageMaker Studio, no customer content is used for service improvement, regardless of whether you use the Free Tier or Pro Tier. For IDE-level telemetry sharing, Amazon Q Developer may track your usage of the service, such as how many questions you ask and whether you accept or reject a recommendation. This information doesn’t contain customer content or personally identifiable information, such as your IP address. If you prefer to opt out of IDE-level telemetry, complete the following steps to opt out of sharing usage data with Amazon Q Developer:

1. On the Settings menu, choose Settings Editor.

2. Uncheck the option Share usage data with Amazon Q Developer.

Alternatively, an ML platform admin can disable this option for all users inside JupyterLab by default with the help of lifecycle configuration scripts. To learn more, see Using lifecycle configurations with JupyterLab. To disable data sharing with Amazon Q Developer by default for all users within a SageMaker Studio domain, complete the following steps:

1. On the SageMaker console, choose Lifecycle configurations under Admin configurations in the navigation pane.
2. Choose Create configuration.

3. For Name, enter a name.
4. In the Scripts section, create a lifecycle configuration script that disables the shareCodeWhispererContentWithAWS settings flag for the jupyterlab-q extension:

#!/bin/bash
mkdir -p /home/sagemaker-user/.jupyter/lab/user-settings/amazon-q-developer-jupyterlab-ext/
cat<<EOL> /home/sagemaker-user/.jupyter/lab/user-settings/amazon-q-developer-jupyterlab-ext/completer.jupyterlab-settings
{
"shareCodeWhispererContentWithAWS": false,   
"suggestionsWithCodeReferences": true,   
"codeWhispererTelemetry": false,
"codeWhispererLogLevel": "ERROR"
}
EOL

5. Attach the disable-q-data-sharing lifecycle configuration to a domain.
6. Optionally, you can force the lifecycle configuration to run with the Run by default

7. Use this lifecycle configuration when creating a JupyterLab space.

It will be selected by default if the configuration is set to Run by default.

The configuration should run almost instantaneously and disable the Share usage data with Amazon Q Developer option in your JupyterLab space on startup.

Clean up

To avoid incurring AWS charges after testing this solution, delete the SageMaker Studio domain.

Conclusion

In this post, we walked through a real-world use case and developed an ML model that predicts the likelihood of readmission after discharge for patients in the Diabetes 130-US hospitals dataset. Throughout this exercise, we used Amazon Q Developer in SageMaker Studio for various stages of the development lifecycle, demonstrating how this developer assistant can help streamline the development process and accelerate time-to-value, even for experienced ML practitioners. You have access to Amazon Q Developer in all AWS Regions where SageMaker is generally available. Get started with Amazon Q Developer in SageMaker Studio today to access the generative AI–powered assistant.

The assistant is available for all Amazon Q Developer Pro and Free Tier users. For pricing information, see Amazon Q Developer pricing.

About the Authors

James Wu is a Senior AI/ML Specialist Solution Architect at AWS. helping customers design and build AI/ML solutions. James’s work covers a wide range of ML use cases, with a primary interest in computer vision, deep learning, and scaling ML across the enterprise. Prior to joining AWS, James was an architect, developer, and technology leader for over 10 years, including 6 years in engineering and 4 years in marketing & advertising industries.

Lauren Mullennex is a Senior AI/ML Specialist Solutions Architect at AWS. She has a decade of experience in DevOps, infrastructure, and ML. Her areas of focus include computer vision, MLOps/LLMOps, and generative AI.

Shibin Michaelraj is a Sr. Product Manager with the Amazon SageMaker team. He is focused on building AI/ML-based products for AWS customers.

Pranav Murthy is an AI/ML Specialist Solutions Architect at AWS. He focuses on helping customers build, train, deploy and migrate machine learning (ML) workloads to SageMaker. He previously worked in the semiconductor industry developing large computer vision (CV) and natural language processing (NLP) models to improve semiconductor processes using state of the art ML techniques. In his free time, he enjoys playing chess and traveling. You can find Pranav on LinkedIn.

Bhadrinath Pani is a Software Development Engineer at Amazon Web Services, working on Amazon SageMaker interactive ML products, with over 12 years of experience in software development across domains like automotive, IoT, AR/VR, and computer vision. Currently, his main focus is on developing machine learning tools aimed at simplifying the experience for data scientists. In his free time, he enjoys spending time with his family and exploring the beauty of the Pacific Northwest.

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With the rise of powerful foundation models (FMs) powered by services such as Amazon Bedrock and Amazon SageMaker JumpStart, enterprises want to exercise granular control over which users and groups can access and use these models. This is crucial for compliance, security, and governance.

Launched in 2021, Amazon SageMaker Canvas is a visual point-and-click service that allows business analysts and citizen data scientists to use ready-to-use machine learning (ML) models and build custom ML models to generate accurate predictions without writing any code. SageMaker Canvas provides a no-code interface to consume a broad range of FMs from both services in an off-the-shelf fashion, as well as to customize model responses using a Retrieval Augmented Generation (RAG) workflow using Amazon Kendra as a knowledge base or fine-tune using a labeled dataset. This simplifies access to generative artificial intelligence (AI) capabilities to business analysts and data scientists without the need for technical knowledge or having to write code, thereby accelerating productivity.

In this post, we analyze strategies for governing access to Amazon Bedrock and SageMaker JumpStart models from within SageMaker Canvas using AWS Identity and Access Management (IAM) policies. You’ll learn how to create granular permissions to control the invocation of ready-to-use Amazon Bedrock models and prevent the provisioning of SageMaker endpoints with specified SageMaker JumpStart models. We provide code examples tailored to common enterprise governance scenarios. By the end, you’ll understand how to lock down access to generative AI capabilities based on your organizational requirements, maintaining secure and compliant use of cutting-edge AI within the no-code SageMaker Canvas environment.

This post covers an increasingly important topic as more powerful AI models become available, making it a valuable resource for ML operators, security teams, and anyone governing AI in the enterprise.

Solution overview

The following diagram illustrates the solution architecture.

The architecture of SageMaker Canvas allows business analysts and data scientists to interact with ML models without writing any code. However, managing access to these models is crucial for maintaining security and compliance. When a user interacts with SageMaker Canvas, the operations they perform, such as invoking a model or creating an endpoint, are run by the SageMaker service role. SageMaker user profiles can either inherit the default role from the SageMaker domain or have a user-specific role.

By customizing the policies attached to this role, you can control what actions are permitted or denied, thereby governing the access to generative AI capabilities. As part of this post, we discuss which IAM policies to use for this role to control operations within SageMaker Canvas, such as invoking models or creating endpoints, based on enterprise organizational requirements. We analyze two patterns for both Amazon Bedrock models and SageMaker JumpStart models: limiting access to all models from a service or limiting access to specific models.

Govern Amazon Bedrock access to SageMaker Canvas

In order to use Amazon Bedrock models, SageMaker Canvas calls the following Amazon Bedrock APIs:

• bedrock:InvokeModel – Invokes the model synchronously
• bedrock:InvokeModelWithResponseStream – Invokes the model synchronously, with the response being streamed over a socket, as illustrated in the following diagram

Additionally, SageMaker Canvas can call the bedrock:FineTune API to fine-tune large language models (LLMs) with Amazon Bedrock. At the time of writing, SageMaker Canvas only allows fine-tuning of Amazon Titan models.

To use a specific LLM from Amazon Bedrock, SageMaker Canvas uses the model ID of the chosen LLM as part of the API calls. At the time of writing, SageMaker Canvas supports the following models from Amazon Bedrock, grouped by model provider:

• AI21
  • Jurassic-2 Mid: j2-mid-v1
  • Jurassic-2 Ultra : j2-ultra-v1
• Amazon
  • Titan: titan-text-premier-v1:*
  • Titan Large: titan-text-lite-v1
  • Titan Express: titan-text-express-v1
• Anthropic
  • Claude 2: claude-v2
  • Claude Instant: claude-instant-v1
• Cohere
  • Command Text: command-text-*
  • Command Light: command-light-text-*
• Meta
  • Llama 2 13B: llama2-13b-chat-v1
  • Llama 2 70B: llama2-70b-chat-v1

For the complete list of models IDs for Amazon Bedrock, see Amazon Bedrock model IDs.

Limit access to all Amazon Bedrock models

To restrict access to all Amazon Bedrock models, you can modify the SageMaker role to explicitly deny these APIs. This makes sure no user can invoke any Amazon Bedrock model through SageMaker Canvas.

The following is an example IAM policy to achieve this:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Deny",
            "Action": [
                "bedrock:InvokeModel",
                "bedrock:InvokeModelWithResponseStream"
            ],
            "Resource": "*"
        }
    ]
}

The policy uses the following parameters:

• "Effect": "Deny" specifies that the following actions are denied
• "Action": ["bedrock:InvokeModel", "bedrock:InvokeModelWithResponseStream"] specifies the Amazon Bedrock APIs that are denied
• "Resource": "*" indicates that the denial applies to all Amazon Bedrock models

Limit access to specific Amazon Bedrock models

You can extend the preceding IAM policy to restrict access to specific Amazon Bedrock models by specifying the model IDs in the Resources section of the policy. This way, users can only invoke the allowed models.

The following is an example of the extended IAM policy:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Deny",
            "Action": [
                "bedrock:InvokeModel",
                "bedrock:InvokeModelWithResponseStream"
            ],
            "Resource": [
                "arn:aws:bedrock:<region-or-*>::foundation-model/<model-id-1>",
                "arn:aws:bedrock:<region-or-*>::foundation-model/<model-id-2>"
            ]
        }
    ]
}

In this policy, the Resource array lists the specific Amazon Bedrock models that are denied. Provide the AWS Region, account, and model IDs appropriate for your environment.

Govern SageMaker JumpStart access to SageMaker Canvas

For SageMaker Canvas to be able to consume LLMs from SageMaker JumpStart, it must perform the following operations:

1. Select the LLM from SageMaker Canvas or from the list of JumpStart Model IDs (link below).
2. Create an endpoint configuration and Deploy the LLM on a real-time endpoint.
3. Invoke the endpoint to generate the prediction.

The following diagram illustrates this workflow.

For a list of available JumpStart model IDs, see JumpStart Available Model Table. At the time of writing, SageMaker Canvas supports the following model IDs:

• huggingface-textgeneration1-mpt-7b-*
• huggingface-llm-mistral-*
• meta-textgeneration-llama-2-*
• huggingface-llm-falcon-*
• huggingface-textgeneration-dolly-v2-*
• huggingface-text2text-flan-t5-*

To identify the right model from SageMaker JumpStart, SageMaker Canvas passes aws:RequestTag/sagemaker-sdk:jumpstart-model-id as part of the endpoint configuration. To learn more about other techniques to limit access to SageMaker JumpStart models using IAM permissions, refer to Manage Amazon SageMaker JumpStart foundation model access with private hubs.

Configure permissions to deploy endpoints through the UI

On the SageMaker domain configuration page on the SageMaker page of the AWS Management Console, you can configure SageMaker Canvas to be able to deploy SageMaker endpoints. This option also enables deployment of real-time endpoints for classic ML models, such as time series forecasting or classification. To enable model deployment, complete the following steps:

1. On the Amazon SageMaker console, navigate to your domain.
2. On the Domain details page, choose the App Configurations

3. In the Canvas section, choose Edit.

4. Turn on Enable direct deployment of Canvas models in the ML Ops configuration

Limit access to all SageMaker JumpStart models

To limit access to all SageMaker JumpStart models, configure the SageMaker role to block the CreateEndpointConfig and CreateEndpoint APIs on any SageMaker JumpStart Model ID. This prevents the creation of endpoints using these models. See the following code:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Deny",
            "Action": [
                "sagemaker:CreateEndpointConfig",
                "sagemaker:CreateEndpoint"
            ],
            "Resource": "*",
"Condition": {
                "Null": {
                    "aws:RequestTag/sagemaker-sdk:jumpstart-model-id":”*”
		    }
		}
        }
    ]
}

This policy uses the following parameters:

• "Effect": "Deny" specifies that the following actions are denied
• "Action": ["sagemaker:CreateEndpointConfig", "sagemaker:CreateEndpoint"] specifies the SageMaker APIs that are denied
• The "Null" condition operator in AWS IAM policies is used to check whether a key exists or not. It does not check the value of the key, only its presence or absence
• "aws:RequestTag/sagemaker-sdk:jumpstart-model-id":”*” indicates that the denial applies to all SageMaker JumpStart models

Limit access and deployment for specific SageMaker JumpStart models

Similar to Amazon Bedrock models, you can limit access to specific SageMaker JumpStart models by specifying their model IDs in the IAM policy. To achieve this, an administrator needs to restrict users from creating endpoints with unauthorized models. For example, to deny access to Hugging Face FLAN T5 models and MPT models, use the following code:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Deny",
            "Action": [
                "sagemaker:CreateEndpointConfig",
                "sagemaker:CreateEndpoint"
            ],
            "Resource": "*",
            "Condition": {
                "StringLike": {
                    "aws:RequestTag/sagemaker-sdk:jumpstart-model-id": [
                        "huggingface-textgeneration1-mpt-7b-*",
                        "huggingface-text2text-flan-t5-*"
                    ]
                }
            }
        }
    ]
}

In this policy, the "StringLike" condition allows for pattern matching, enabling the policy to apply to multiple model IDs with similar prefixes.

Clean up

To avoid incurring future workspace instance charges, log out of SageMaker Canvas when you’re done using the application. Optionally, you can configure SageMaker Canvas to automatically shut down when idle.

Conclusion

In this post, we demonstrated how SageMaker Canvas invokes LLMs powered by Amazon Bedrock and SageMaker JumpStart, and how enterprises can govern access to these models, whether you want to limit access to specific models or to any model from either service. You can combine the IAM policies shown in this post in the same IAM role to provide complete control.

By following these guidelines, enterprises can make sure their use of generative AI models is both secure and compliant with organizational policies. This approach not only safeguards sensitive data but also empowers business analysts and data scientists to harness the full potential of AI within a controlled environment.

Now that your environment is configured according to the enterprise standard, we suggest reading the following posts to learn what SageMaker Canvas enables you to do with generative AI:

• Prioritizing employee well-being: An innovative approach with generative AI and Amazon SageMaker Canvas
• Fine-tune and deploy language models with Amazon SageMaker Canvas and Amazon Bedrock
• Analyze security findings faster with no-code data preparation using generative AI and Amazon SageMaker Canvas
• Overcoming common contact center challenges with generative AI and Amazon SageMaker Canvas
• Empower your business users to extract insights from company documents using Amazon SageMaker Canvas and Generative AI

About the Authors

Davide Gallitelli is a Senior Specialist Solutions Architect GenAI/ML. He is Italian, based in Brussels, and works closely with customer all around the world on Generative AI workloads and Low-Code No-Code ML technology. He has been a developer since very young, starting to code at the age of 7. He started learning AI/ML in his later years of university, and has fallen in love with it since then.

Lijan Kuniyil is a Senior Technical Account Manager at AWS. Lijan enjoys helping AWS enterprise customers build highly reliable and cost-effective systems with operational excellence. Lijan has more than 25 years of experience in developing solutions for financial and consulting companies.

Saptarshi Banerjee serves as a Senior Partner Solutions Architect at AWS, collaborating closely with AWS Partners to design and architect mission-critical solutions. With a specialization in generative AI, AI/ML, serverless architecture, and cloud-based solutions, Saptarshi is dedicated to enhancing performance, innovation, scalability, and cost-efficiency for AWS Partners within the cloud ecosystem.

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