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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 126
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Use LangChain with PySpark to process documents at massive scale with Amazon SageMaker Studio and Amazon EMR Serverless

AWS Machine Learning

Harnessing the power of big data has become increasingly critical for businesses looking to gain a competitive edge. However, managing the complex infrastructure required for big data workloads has traditionally been a significant challenge, often requiring specialized expertise.

Big data 116
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Accelerate analysis and discovery of cancer biomarkers with Amazon Bedrock Agents

AWS Machine Learning

We showcase a variety of tools including database retrieval with Text2SQL, statistical models and visual charts with scientific libraries, biomedical literature search with public APIs and internal evidence, and medical image processing with Amazon SageMaker jobs. Each data modality presents a different view of a patient.

APIs 67
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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 start by using the SageMaker Studio UI and then by using APIs.

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Secure Amazon SageMaker Studio presigned URLs Part 1: Foundational infrastructure

AWS Machine Learning

This presents an undesired threat vector for exfiltration and gaining access to customer data when proper access controls are not enforced. Studio supports a few methods for enforcing access controls against presigned URL data exfiltration: Client IP validation using the IAM policy condition aws:sourceIp. About the Authors.

APIs 91
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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. In this post, we present an example multi-account architecture for developing and deploying ML workflows with SageMaker Pipelines.

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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 105