ml-workflowlisted

# ML Workflow ## Purpose Design end-to-end ML workflows covering experiment tracking, feature engineering and storage, model training pipelines, model serving and deployment, A/B testing for models, and monitoring for data and model drift. Produces a workflow architecture, tool selection rationale, and operational runbook. ## Scope Constraints Reads ML code, configuration files, experiment logs, and infrastructure specs for analysis. Does not train models, execute experiments, or deploy to production. ## Inputs - ML problem type (classification, regression, ranking, recommendation, NLP, CV) - Data sources and feature candidates - Model complexity range (linear/tree-based vs deep learning) - Serving requirements (batch predictions, real-time inference, edge deployment) - Team size and ML maturity (first model vs established ML platform) - Infrastructure constraints (cloud provider, GPU availability, budget) ## Input Sanitization No user-provided values are used in commands or file paths. All inputs are treated as read-only analysis targets. ## Procedure ### Progress Checklist - [ ] Step 1: Define the ML problem - [ ] Step 2: Design feature engineering pipeline - [ ] Step 3: Design experiment tracking - [ ] Step 4: Design training pipeline - [ ] Step 5: Design model serving - [ ] Step 6: Design A/B testing - [ ] Step 7: Design monitoring and drift detection ### Step 1: Define the ML Problem Clearly Before any tooling decisions, formalize: - What is the prediction ta