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

AWS Machine Learning

The solution integrates large language models (LLMs) with your organization’s data and provides an intelligent chat assistant that understands conversation context and provides relevant, interactive responses directly within the Google Chat interface. The following figure illustrates the high-level design of the solution.

APIs 120
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Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning

Customers can use the SageMaker Studio UI or APIs to specify the SageMaker Model Registry model to be shared and grant access to specific AWS accounts or to everyone in the organization. We will demonstrate how to set up a central model registry based on the architecture we described in the previous sections.

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Secure Amazon SageMaker Studio presigned URLs Part 2: Private API with JWT authentication

AWS Machine Learning

In part 1 of this series, we demonstrated how to resolve an Amazon SageMaker Studio presigned URL from a corporate network using Amazon private VPC endpoints without traversing the internet. The user invokes createStudioPresignedUrl API on API Gateway along with a token in the header. Deploy the solution. Deploy the solution.

APIs 94
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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 69
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Reducing hallucinations in LLM agents with a verified semantic cache using Amazon Bedrock Knowledge Bases

AWS Machine Learning

Solution overview Our solution implements a verified semantic cache using the Amazon Bedrock Knowledge Bases Retrieve API to reduce hallucinations in LLM responses while simultaneously improving latency and reducing costs. The function checks the semantic cache (Amazon Bedrock Knowledge Bases) using the Retrieve API.

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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 85
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Use AWS PrivateLink to set up private access to Amazon Bedrock

AWS Machine Learning

It allows developers to build and scale generative AI applications using FMs through an API, without managing infrastructure. Customers are building innovative generative AI applications using Amazon Bedrock APIs using their own proprietary data.

APIs 140