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Governing ML lifecycle at scale: Best practices to set up cost and usage visibility of ML workloads in multi-account environments

AWS Machine Learning

For a multi-account environment, you can track costs at an AWS account level to associate expenses. A combination of an AWS account and tags provides the best results. For multiple accounts, assign mandatory tags to each one, identifying its purpose and the owner responsible.

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Best practices for building secure applications with Amazon Transcribe

AWS Machine Learning

Because these best practices might not be appropriate or sufficient for your environment, use them as helpful considerations rather than prescriptions. Applications must have valid credentials to sign API requests to AWS services. The customer data is cleaned up for both complete and failure cases.

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Security best practices to consider while fine-tuning models in Amazon Bedrock

AWS Machine Learning

In this post, we delve into the essential security best practices that organizations should consider when fine-tuning generative AI models. The workflow steps are as follows: The user submits an Amazon Bedrock fine-tuning job within their AWS account, using IAM for resource access. Choose Create policy.

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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.

APIs 129
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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.

APIs 130
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Best practices to build generative AI applications on AWS

AWS Machine Learning

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon via a single API. This is because such tasks require organization-specific data and workflows that typically need custom programming.

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Drive efficiencies with CI/CD best practices on Amazon Lex

AWS Machine Learning

You liked the overall experience and now want to deploy the bot in your production environment, but aren’t sure about best practices for Amazon Lex. In this post, we review the best practices for developing and deploying Amazon Lex bots, enabling you to streamline the end-to-end bot lifecycle and optimize your operations.