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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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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 126
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Use RAG for drug discovery with Knowledge Bases for Amazon Bedrock

AWS Machine Learning

Knowledge Bases for Amazon Bedrock supports multiple vector databases, including Amazon OpenSearch Serverless , Amazon Aurora , Pinecone, and Redis Enterprise Cloud. For enterprise implementations, Knowledge Bases supports AWS Key Management Service (AWS KMS) encryption, AWS CloudTrail integration, and more.

APIs 115
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Automate caption creation and search for images at enterprise scale using generative AI and Amazon Kendra

AWS Machine Learning

An example of a customized image search is Enterprise Resource Planning (ERP). In ERP, image data collected from different stages of logistics or supply chain management could include tax receipts, vendor orders, payslips, and more, which need to be automatically categorized for the purview of different teams within the organization.

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Secure Amazon SageMaker Studio presigned URLs Part 1: Foundational infrastructure

AWS Machine Learning

These traffic routing products generate their own source IP, whose IP range is not controlled by the enterprise customer. This makes it impossible for these enterprise customers to use the aws:sourceIp condition. The corporate portal application makes a private API call using an API Gateway VPC endpoint to create a presigned URL.

APIs 82
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Use Amazon SageMaker pipeline sharing to view or manage pipelines across AWS accounts

AWS Machine Learning

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. The data scientist is now able to describe and monitor the test pipeline run status using SageMaker API calls from the dev account.

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Identify objections in customer conversations using Amazon Comprehend to enhance customer experience without ML expertise

AWS Machine Learning

In this post, we explore how AWS customer Pro360 used the Amazon Comprehend custom classification API , which enables you to easily build custom text classification models using your business-specific labels without requiring you to learn machine learning (ML), to improve customer experience and reduce operational costs.

APIs 63