ml-pipeline-workflow

Automate an end-to-end ML pipeline by defining stages for data ingestion, validation, feature engineering, training, validation, and deployment. Configure dependencies between these stages to establish clear data flow and enable production-grade workflow orchestration.

DAG-based orchestration for MLOps workflows structures machine learning tasks into a directed acyclic graph to manage complex data flow. This architecture ensures reproducible, scalable pipelines by organizing dependencies across frameworks like Airflow, Dagster, or Kubeflow.

Yes, the ML pipeline workflow supports DAG-based orchestration using Airflow, Dagster, or Kubeflow. You can configure dependencies and reference the pipeline-dag.yaml template to implement end-to-end MLOps workflows within your preferred orchestration framework.

Set up canary or blue-green deployment for machine learning models using deployment automation frameworks within the pipeline. This approach manages serving patterns and provides rollback mechanisms after model validation and A/B testing readiness checks are completed.

Include data validation and feature engineering in an automated MLOps pipeline by defining them as distinct pipeline stages following data ingestion. This ensures data validation, feature engineering, versioning, and reproducible preprocessing occur before model training orchestration begins.