Ml Pipeline Workflow

You're setting up the whole MLOps lifecycle and need something that covers data prep through deployment in one coherent workflow. This walks you through orchestrating pipelines with Airflow, Dagster, or Kubeflow, handling everything from data validation and feature engineering to training jobs, model validation, and gradual rollouts. It includes DAG templates, validation checklists, and references for each stage. The progressive disclosure approach is smart, starting with simple linear pipelines before adding A/B testing and ensemble strategies. Best for greenfield ML infrastructure or when you're standardizing scattered workflows into a proper end-to-end system with proper versioning and monitoring built in.