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 a single natural-language workflow across your AWS environment. 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, replacing previous AWS Labs skills and plugins.
• Extended SageMaker AI capability areas supported by skills:

• 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.

The AWS AI/ML skill is available at no additional charge; you pay only for the AWS resources your agents use. To get started, install the skill from the Agent Toolkit for AWS on GitHub, or use it pre-installed in SageMaker Studio JupyterLab.

What to do

• Install the AWS AI/ML skill from the Agent Toolkit for AWS on GitHub.
• Use the skill pre-installed in SageMaker Studio JupyterLab.
• Refer to the Amazon SageMaker AI documentation for using a coding assistant with your SageMaker AI workflows.

Source: AWS release notes

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