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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. Customers are building innovative generative AI applications using Amazon Bedrock APIs using their own proprietary data.

APIs 130
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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 81
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Govern generative AI in the enterprise with Amazon SageMaker Canvas

AWS Machine Learning

To use a specific LLM from Amazon Bedrock, SageMaker Canvas uses the model ID of the chosen LLM as part of the API calls. Limit access to all Amazon Bedrock models To restrict access to all Amazon Bedrock models, you can modify the SageMaker role to explicitly deny these APIs. This way, users can only invoke the allowed models.

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Getting started with cross-region inference in Amazon Bedrock

AWS Machine Learning

Moreover, this capability prioritizes the connected Amazon Bedrock API source/primary region when possible, helping to minimize latency and improve responsiveness. Compatibility with existing Amazon Bedrock API No additional routing or data transfer cost and you pay the same price per token for models as in your source/primary region.

APIs 136
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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. We do so using AWS SDK for Python (Boto3) CreateProcessingJob API calls.

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Fine-tune Anthropic’s Claude 3 Haiku in Amazon Bedrock to boost model accuracy and quality

AWS Machine Learning

Run the fine-tuning job using the Amazon Bedrock API Make sure to request access to the preview of Anthropic Claude 3 Haiku fine-tuning in Amazon Bedrock, as discussed in the prerequisites. Prerequisites To use this feature, make sure you have satisfied the following requirements: An active AWS account.

APIs 120