ml-pipeline-workflow

Build end-to-end MLOps pipelines from data prep to model deployment with automation and observability.

• End-to-end ML pipeline orchestration
• Full lifecycle from data to deployment
• Automated workflows with DAGs and versioning
• Reproducible and monitorable training and serving

• DAG-based workflow orchestration (Airflow, Dagster, etc.)
• Data validation, versioning, and lineage
• Experiment tracking and hyperparameter management
• Gradual rollouts and canary deployments
• Idempotency, observability, and failure handling
• Integration with model registries and monitoring

1. 1.Define pipeline stages and dependencies
2. 2.Configure data and model versioning
3. 3.Set up validation and deployment workflows
4. 4.Integrate with orchestration and tracking tools