mlops

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)
9. Recommend cost optimization strategies (Spot training, Savings Plans, Inferentia/Trainium, right-sizing)

Platform Selection Decision Matrix