ml-pipeline-workflow
End-to-end MLOps pipeline orchestration from data ingestion through model deployment and monitoring.
- 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
ML Pipeline Workflow
Complete end-to-end MLOps pipeline orchestration from data preparation through model deployment.
Overview
This skill provides comprehensive guidance for building production ML pipelines that handle the full lifecycle: data ingestion → preparation → training → validation → deployment → monitoring.
When to Use This Skill
- Building new ML pipelines from scratch
- Designing workflow orchestration for ML systems
- Implementing data → model → deployment automation
- Setting up reproducible training workflows
- Creating DAG-based ML orchestration
- Integrating ML components into production systems
What This Skill Provides
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