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mlops

Skill

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

0Machine Learning

Install

git clone https://github.com/aws-samples/sample-claude-code-plugins-for-startups.git ~/.claude/skills/mlops

What is mlops?

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.

What this can do

Capabilities declared in this component's own frontmatter — not inferred.

Inherit all session tools

Declares no tool restrictions — inherits every session tool

~162 tokens of context used while enabled, before you invoke anything

Documentation

README · ~15 min read

Specialist guidance for MLOps on AWS. Covers platform selection, training job configuration, inference deployment patterns, CI/CD for ML, experiment tracking, model monitoring, and cost optimization.

Process

  1. Identify the ML workload characteristics: model type (classical ML, deep learning, foundation model), training data volume, inference latency requirements, traffic pattern, team expertise
  2. Use the awsknowledge MCP tools (mcp__plugin_aws-dev-toolkit_awsknowledge__aws___search_documentation, mcp__plugin_aws-dev-toolkit_awsknowledge__aws___read_documentation, mcp__plugin_aws-dev-toolkit_awsknowledge__aws___recommend) to verify current SageMaker instance types, limits, pricing, and feature availability
  3. Select the appropriate MLOps platform using the decision matrix below
  4. Design the training infrastructure (instance selection, distributed strategy, Spot configuration)
  5. Design the inference topology (real-time, serverless, batch, async)
  6. Configure the ML pipeline (SageMaker Pipelines, Step Functions, or CI/CD integration)
  7. Set up experiment tracking (MLflow on SageMaker or SageMaker Experiments)
  8. Configure model monitoring (data quality, model quality, bias drift, feature attribution drift)

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