Your coding agent can select, train, optimize and deploy models on Amazon SageMaker AI while working across the rest of your AWS environment in a single natural-language workflow. The AWS AI/ML skill is now a core skill in the Agent Toolkit for AWS, so it works alongside the toolkit’s other AWS skills like EC2, Bedrock, and more.
We released the new AWS AI/ML skill on the Agent Toolkit for AWS. The toolkit is now the single, supported integration point, replacing the previous AWS Labs skills and plugins, with a consistent and easy install and update path. The skill is also pre-installed into SageMaker Studio JupyterLab for use with Kiro or your coding agent of choice.
Moreover, we extended the SageMaker AI capability areas supported by skills.
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Inference optimization: optimize deployed models for latency, throughput, and cost
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Model customization: end-to-end guided fine-tuning workflows, from use case definition through training, evaluation and deployment
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Serverful training for both traditional ML and generative AI
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MLflow: experiment tracking and model lifecycle management
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JumpStart model deployment and model recommendations: deploy pre-built JumpStart models and get guided recommendations on the right model for the use case
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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. To learn more about using a coding assistant with your SageMaker AI workflows, see the Amazon SageMaker AI documentation.
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Source: Amazon Web Services
