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Build a secure enterprise application with Generative AI and RAG using Amazon SageMaker JumpStart

AWS Machine Learning

In this post, we build a secure enterprise application using AWS Amplify that invokes an Amazon SageMaker JumpStart foundation model, Amazon SageMaker endpoints, and Amazon OpenSearch Service to explain how to create text-to-text or text-to-image and Retrieval Augmented Generation (RAG). You may need to request a quota increase.

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Harnessing the power of enterprise data with generative AI: Insights from Amazon Kendra, LangChain, and large language models

AWS Machine Learning

Furthermore, the cost to train new LLMs can prove prohibitive for many enterprise settings. These demos can be seamlessly deployed in your AWS account, offering foundational insights and guidance on utilizing AWS services to create a state-of-the-art LLM generative AI question and answer bot and content generation. Python 3.6

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

AWS Machine Learning

In this post, we will continue to build on top of the previous solution to demonstrate how to build a private API Gateway via Amazon API Gateway as a proxy interface to generate and access Amazon SageMaker presigned URLs. The user invokes createStudioPresignedUrl API on API Gateway along with a token in the header.

APIs 84
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Amazon SageMaker Feature Store now supports cross-account sharing, discovery, and access

AWS Machine Learning

SageMaker Feature Store now makes it effortless to share, discover, and access feature groups across AWS accounts. With this launch, account owners can grant access to select feature groups by other accounts using AWS Resource Access Manager (AWS RAM).

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Use Amazon SageMaker pipeline sharing to view or manage pipelines across AWS accounts

AWS Machine Learning

On August 9, 2022, we announced the general availability of cross-account sharing of Amazon SageMaker Pipelines entities. You can now use cross-account support for Amazon SageMaker Pipelines to share pipeline entities across AWS accounts and access shared pipelines directly through Amazon SageMaker API calls.

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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 124
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Get more out of SuccessBLOCS and APIs

Totango

Stream Account & User Tag Information Using Customer Data Hub API. Tags are used to automatically highlight important insights about accounts and users and play an important role in triggering automation like SuccessPlays and Campaigns. Now, tag information can be easily streamed to Totango via Customer Data Hub API.

APIs 62