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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.

APIs 118
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How Formula 1® uses generative AI to accelerate race-day issue resolution

AWS Machine Learning

During these live events, F1 IT engineers must triage critical issues across its services, such as network degradation to one of its APIs. This impacts downstream services that consume data from the API, including products such as F1 TV, which offer live and on-demand coverage of every race as well as real-time telemetry.

APIs 70
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Secure Amazon SageMaker Studio presigned URLs Part 3: Multi-account private API access to Studio

AWS Machine Learning

In the post Secure Amazon SageMaker Studio presigned URLs Part 2: Private API with JWT authentication , we demonstrated how to build a private API to generate Amazon SageMaker Studio presigned URLs that are only accessible by an authenticated end-user within the corporate network from a single account.

APIs 81
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Build a custom UI for Amazon Q Business

AWS Machine Learning

This solution uses an Amazon Cognito user pool as an OAuth-compatible identity provider (IdP), which is required in order to exchange a token with AWS IAM Identity Center and later on interact with the Amazon Q Business APIs. Amazon Q uses the chat_sync API to carry out the conversation.

APIs 128
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Use RAG for drug discovery with Knowledge Bases for Amazon Bedrock

AWS Machine Learning

The Retrieve and RetrieveAndGenerate APIs allow your applications to directly query the index using a unified and standard syntax without having to learn separate APIs for each different vector database, reducing the need to write custom index queries against your vector store.

APIs 131
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Generate images from text with the stable diffusion model on Amazon SageMaker JumpStart

AWS Machine Learning

We explore two ways of obtaining the same result: via JumpStart’s graphical interface on Amazon SageMaker Studio , and programmatically through JumpStart APIs. If you want to jump straight into the JumpStart API code we go through in this post, you can refer to the following sample Jupyter notebook: Introduction to JumpStart – Text to Image.

APIs 98
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Run text generation with GPT and Bloom models on Amazon SageMaker JumpStart

AWS Machine Learning

We explore two ways of obtaining the same result: via JumpStart’s graphical interface on Amazon SageMaker Studio , and programmatically through JumpStart APIs. The following sections provide a step-by-step demo to perform inference, both via the Studio UI and via JumpStart APIs. JumpStart overview. Solution overview.

APIs 92