Bring deep SageMaker AI expertise to your coding agent with the AWS AI/ML skill in the Agent Toolkit for AWS

AWS Release Notes

Your coding agent can now select, train, optimize, and deploy models on Amazon SageMaker AI within your AWS environment using a single natural-language workflow. The AWS AI/ML skill is now a core skill in the Agent Toolkit for AWS, working alongside other AWS skills like EC2 and Bedrock.

New Features

• The AWS AI/ML skill is now a core skill in the Agent Toolkit for AWS.
• Extended SageMaker AI capability areas supported by skills, including:
• Inference optimization: optimize deployed models for latency, throughput, and cost
• Model customization: end-to-end guided fine-tuning workflows, from use case definition through training, evaluation, and deployment
• Serverful training: for both traditional ML and generative AI
• MLflow: experiment tracking and model lifecycle management
• JumpStart model deployment: deploy pre-built JumpStart models and get guided recommendations on the right model for the use case
• Processing jobs: data preparation and ETL on managed compute

What to do

• Install the skill from the Agent Toolkit for AWS on GitHub.
• Use it pre-installed in SageMaker Studio JupyterLab.
• Learn more about using a coding assistant with your SageMaker AI workflows in the Amazon SageMaker AI documentation.

Source: AWS release notes

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