ml-pipeline-workflow

End-to-end MLOps pipeline orchestration from data ingestion through model deployment and monitoring. Complete end-to-end MLOps pipeline orchestration from data preparation through model deployment. This skill provides comprehensive guidance for building production ML pipelines that handle the full lifecycle: data ingestion → preparation → training → validation → deployment → monitoring. Pipeline Architecture Data Preparation Model Training Model Validation Deployment Automation See the references/ directory for detailed guides: The assets/ directory contains: Data Preparation Phase Training Phase Validation Phase Deployment Phase Start with the basics and gradually add complexity: After setting up your pipeline: - Covers five core pipeline stages: data preparation, model training, validation, deployment, and monitoring with DAG orchestration patterns (Airflow, Dagster, Kubeflow) - Includes data validation, feature engineering, experiment tracking integration, and model versioning strategies across the full ML lifecycle - Provides deployment automation patterns including canary releases, blue-green deployments, A/B testing infrastructure, and rollback mechanisms - References and templates available for pipeline DAGs, training configuration, and pre-deployment validation checklists - Building new ML pipelines from scratch - Designing workflow orchestration for ML systems -...