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Best practices and lessons for fine-tuning Anthropic’s Claude 3 Haiku on Amazon Bedrock

AWS Machine Learning

In this post, we explore the best practices and lessons learned for fine-tuning Anthropic’s Claude 3 Haiku on Amazon Bedrock. Structured outputs – For example, when you have 10,000 labeled examples specific to your use case and need Anthropic’s Claude 3 Haiku to accurately identify them. Sonnet across various tasks.

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Use the ApplyGuardrail API with long-context inputs and streaming outputs in Amazon Bedrock

AWS Machine Learning

The new ApplyGuardrail API enables you to assess any text using your preconfigured guardrails in Amazon Bedrock, without invoking the FMs. In this post, we demonstrate how to use the ApplyGuardrail API with long-context inputs and streaming outputs. For example, you can now use the API with models hosted on Amazon SageMaker.

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Best practices for building secure applications with Amazon Transcribe

AWS Machine Learning

Amazon Transcribe can be used for transcription of customer care calls, multiparty conference calls, and voicemail messages, as well as subtitle generation for recorded and live videos, to name just a few examples. Applications must have valid credentials to sign API requests to AWS services.

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Best practices for building robust generative AI applications with Amazon Bedrock Agents – Part 1

AWS Machine Learning

This two-part series explores best practices for building generative AI applications using Amazon Bedrock Agents. This data provides a benchmark for expected agent behavior, including the interaction with existing APIs, knowledge bases, and guardrails connected with the agent.

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Streamline workflow orchestration of a system of enterprise APIs using chaining with Amazon Bedrock Agents

AWS Machine Learning

Intricate workflows that require dynamic and complex API orchestration can often be complex to manage. In this post, we explore how chaining domain-specific agents using Amazon Bedrock Agents can transform a system of complex API interactions into streamlined, adaptive workflows, empowering your business to operate with agility and precision.

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Best practices for building robust generative AI applications with Amazon Bedrock Agents – Part 2

AWS Machine Learning

In Part 1 of this series, we explored best practices for creating accurate and reliable agents using Amazon Bedrock Agents. The agent can use company APIs and external knowledge through Retrieval Augmented Generation (RAG). If you already have an OpenAPI schema for your application, the best practice is to start with it.

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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. and create-iam-oidc-qbiz-app.py

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