End-to-end MLOps guidance on AWS — platform selection, training, inference, pipelines, monitoring, and cost optimization. This skill should be used when the user asks to "build an ML pipeline", "deploy a model on SageMaker", "set up MLOps", "configure SageMaker Pipelines", "choose between SageMaker and Bedrock", "deploy ML models to production", "set up model monitoring", "use MLflow on AWS", "train a model with Spot instances", "configure inference endpoints", "set up distributed training", or mentions SageMaker, MLflow, Kubeflow, ML pipelines, model registry, model monitoring, hyperparameter tuning, inference endpoints, or MLOps on AWS.
How to install mlops?
aws-samples/sample-claude-code-plugins-for-startups/mlops
$npx -y skills add aws-samples/sample-claude-code-plugins-for-startups --skill mlopsInstalls into the current project.
›Prefer a prompt? Paste this to your agent
Run `npx skills use "https://github.com/aws-samples/sample-claude-code-plugins-for-startups" --skill "aws-samples/sample-claude-code-plugins-for-startups/mlops"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.
Use the skills in "https://github.com/aws-samples/sample-claude-code-plugins-for-startups" that are relevant to the current task. Run `npx skills add "https://github.com/aws-samples/sample-claude-code-plugins-for-startups"` and select the relevant skills, then follow their instructions.
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