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Implement secure API access to your Amazon Q Business applications with IAM federation user access management

AWS Machine Learning

Amazon Q Business provides a rich set of APIs to perform administrative tasks and to build an AI assistant with customized user experience for your enterprise. In this post, we show how to use Amazon Q Business APIs when using AWS Identity and Access Management (IAM) federation for user access management. The sample scripts samlapp.py

APIs 78
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Integrate dynamic web content in your generative AI application using a web search API and Amazon Bedrock Agents

AWS Machine Learning

Amazon Bedrock agents use LLMs to break down tasks, interact dynamically with users, run actions through API calls, and augment knowledge using Amazon Bedrock Knowledge Bases. In this post, we demonstrate how to use Amazon Bedrock Agents with a web search API to integrate dynamic web content in your generative AI application.

APIs 105
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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 125
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Few-shot prompt engineering and fine-tuning for LLMs in Amazon Bedrock

AWS Machine Learning

Earnings calls are live conferences where executives present an overview of results, discuss achievements and challenges, and provide guidance for upcoming periods. Traditionally, earnings call scripts have followed similar templates, making it a repeatable task to generate them from scratch each time.

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Amazon Bedrock Custom Model Import now generally available

AWS Machine Learning

This feature empowers customers to import and use their customized models alongside existing foundation models (FMs) through a single, unified API. However, hosting models presents its own unique set of challenges. Having a unified developer experience when accessing custom models or base models through Amazon Bedrock’s API.

APIs 139
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Bring legacy machine learning code into Amazon SageMaker using AWS Step Functions

AWS Machine Learning

The best practice for migration is to refactor these legacy codes using the Amazon SageMaker API or the SageMaker Python SDK. SageMaker runs the legacy script inside a processing container. Step Functions is a serverless workflow service that can control SageMaker APIs directly through the use of the Amazon States Language.

Scripts 137
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Create a document lake using large-scale text extraction from documents with Amazon Textract

AWS Machine Learning

The first allows you to run a Python script from any server or instance including a Jupyter notebook; this is the quickest way to get started. In the following sections, we first describe the script solution, followed by the AWS CDK construct solution. The following diagram illustrates the sequence of events within the script.

Scripts 108