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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. By using fine-tuning capabilities, businesses can unlock the full potential of generative AI while maintaining control over the models behavior and aligning it with their goals and values.

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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. You can choose from various FMs from Amazon and leading AI startups such as AI21 Labs, Anthropic, Cohere, and Stability AI to find the model that’s best suited for your use case.

APIs 141
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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 98
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Build a multilingual automatic translation pipeline with Amazon Translate Active Custom Translation

AWS Machine Learning

We demonstrate how to use the AWS Management Console and Amazon Translate public API to deliver automatic machine batch translation, and analyze the translations between two language pairs: English and Chinese, and English and Spanish. In this post, we present a solution that D2L.ai

APIs 102
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Architecture to AWS CloudFormation code using Anthropic’s Claude 3 on Amazon Bedrock

AWS Machine Learning

The workflow consists of the following steps: The user uploads an architecture image (JPEG or PNG) on the Streamlit application, invoking the Amazon Bedrock API to generate a step-by-step explanation of the architecture using the Anthropic’s Claude 3 Sonnet model. The following diagram illustrates the step-by-step process.

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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. Step Functions is a serverless workflow service that can control SageMaker APIs directly through the use of the Amazon States Language.

Scripts 144
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Securing MLflow in AWS: Fine-grained access control with AWS native services

AWS Machine Learning

In this post, we address these limitations by implementing the access control outside of the MLflow server and offloading authentication and authorization tasks to Amazon API Gateway , where we implement fine-grained access control mechanisms at the resource level using Identity and Access Management (IAM). Adds an IAM authorizer.

APIs 97