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Amazon Bedrock launches Session Management APIs for generative AI applications (Preview)

AWS Machine Learning

Amazon Bedrock announces the preview launch of Session Management APIs, a new capability that enables developers to simplify state and context management for generative AI applications built with popular open source frameworks such as LangGraph and LlamaIndex.

APIs 98
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Enhance customer support with Amazon Bedrock Agents by integrating enterprise data APIs

AWS Machine Learning

In this post, we guide you through integrating Amazon Bedrock Agents with enterprise data APIs to create more personalized and effective customer support experiences. An automotive retailer might use inventory management APIs to track stock levels and catalog APIs for vehicle compatibility and specifications.

APIs 127
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Enable Amazon Bedrock cross-Region inference in multi-account environments

AWS Machine Learning

Cross-Region inference enables you to seamlessly manage unplanned traffic bursts by utilizing compute across different Regions. Importantly, cross-Region inference prioritizes the connected Amazon Bedrock API source Region when possible, helping minimize latency and improve overall responsiveness.

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Create a generative AI–powered custom Google Chat application using Amazon Bedrock

AWS Machine Learning

The custom Google Chat app, configured for HTTP integration, sends an HTTP request to an API Gateway endpoint. Before processing the request, a Lambda authorizer function associated with the API Gateway authenticates the incoming message. The following figure illustrates the high-level design of the solution.

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Build a multi-tenant generative AI environment for your enterprise on AWS

AWS Machine Learning

It also uses a number of other AWS services such as Amazon API Gateway , AWS Lambda , and Amazon SageMaker. It contains services used to onboard, manage, and operate the environment, for example, to onboard and off-board tenants, users, and models, assign quotas to different tenants, and authentication and authorization microservices.

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Track LLM model evaluation using Amazon SageMaker managed MLflow and FMEval

AWS Machine Learning

Ultimately, this systematic approach to managing models, prompts, and datasets contributes to the development of more reliable and transparent generative AI applications. MLflow is an open source platform for managing the end-to-end ML lifecycle, including experimentation, reproducibility, and deployment.

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Integrate generative AI capabilities into Microsoft Office using Amazon Bedrock

AWS Machine Learning

One can quickly host such application on the AWS Cloud without managing the underlying infrastructure, for example, with Amazon Simple Storage Service (S3) and Amazon CloudFront. Note that these APIs use objects as namespaces, alleviating the need for explicit imports. For testing, one can sideload an Office Add-in. Sonnet).

APIs 98