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

<p>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 <a href="https://github.com/aws/agent-toolkit-for-aws/tree/main">Agent Toolkit for AWS</a>, so it works alongside the toolkit's other AWS skills like EC2, Bedrock, and more.</p> <p>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.</p> <p>Moreover, we extended the SageMaker AI capability areas supported by skills.</p> <ul> <li><p><a href="https://aws.amazon.com/blogs/machine-learning/new-agent-skill-amazon-sagemaker-optimized-generative-ai-inference-for-your-coding-agent/">Inference optimization</a>: optimize deployed models for latency, throughput, and cost</p> </li> <li><p><a href="https://builder.aws.com/content/3BRdZnv6dY63soyl4qn7FJMQdqd/agentic-model-customization-simplifying-fine-tuning-with-ai-guided-workflows-in-amazon-sagemaker-ai">Model customization</a>: end-to-end guided fine-tuning workflows, from use case definition through training, evaluation and deployment</p> </li> <li><p>Serverful training for both traditional ML and generative AI</p> </li> <li><p>MLflow: experiment tracking and model lifecycle management</p> </li> <li><p>JumpStart model deployment and model recommendations: deploy pre-built JumpStart models and get guided recommendations on the right model for the use case</p> </li> <li><p>Processing jobs: data preparation and ETL on managed compute</p> </li> </ul> <p>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 <a href="https://github.com/aws/agent-toolkit-for-aws/tree/main">Agent Toolkit for AWS</a> on GitHub, or use it pre-installed in SageMaker Studio JupyterLab. To learn more about <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/studio-updated-jl-coding-assistant.html">using a coding assistant with your SageMaker AI workflows</a>, see the Amazon SageMaker AI documentation.</p>