ml-pipeline-workflow

An end-to-end MLOps pipeline automates the full machine learning lifecycle: data ingestion, preparation, model training, validation, deployment, and monitoring. This Skill provides a framework covering orchestration patterns (Airflow, Dagster, Kubeflow), data quality checks, feature engineering, experiment tracking, versioning, and deployment strategies like canary or blue-green with rollback capabilities.

Reproducible workflows require versioning data, code, and models throughout the pipeline. This Skill emphasizes tracking experiments, managing dependencies, and automating retraining cycles so results remain consistent across environments and team members can reliably reproduce training outcomes.

Yes. This Skill covers deployment automation using canary and blue-green strategies with robust rollback mechanisms, enabling safe model releases. It also supports A/B testing capabilities for validating new models before full production rollout.

This Skill addresses DAG-based orchestration patterns used by Airflow, Dagster, and Kubeflow. It guides pipeline design, workflow component orchestration, and automation across these frameworks for production-grade MLOps implementations.

Data validation and quality checks are core stages in the pipeline framework, applied before model training. Feature engineering integrates into the data preparation phase, ensuring clean, transformed data flows through orchestrated workflow components to training and deployment.

This Skill incorporates model and data versioning to track changes over time, plus robust monitoring for production-deployed models. Versioning enables rollback if issues arise, while monitoring tracks model performance and data drift to trigger retraining when needed.