ml-pipeline-workflow

An end-to-end ML pipeline automates data ingestion, train/validation splits, model training, evaluation, and deployment to a serving endpoint. You can orchestrate these steps as a DAG using tools like Airflow, Dagster, or Kubeflow with modular components and error handling.

DAG-based orchestration structures MLOps workflows as directed acyclic graphs, mapping out dependencies between data preparation, model training, validation, and deployment. This approach ensures modular execution, robust error handling, and reproducible machine learning operations.

Yes, ML pipelines support integration with experiment tracking tools like MLflow and Weights & Biases. This allows you to log metrics, compare model versions, and maintain reproducible, auditable workflows throughout the training and validation phases.

Yes, the pipeline supports versioning of both data and models. Combined with robust error handling and orchestration tools like Airflow or Kubeflow, this ensures your training runs are reproducible and your deployment artifacts are fully auditable.

Automating model validation and deployment requires orchestrating these steps within a DAG-based workflow using tools like Dagster or Kubeflow. The pipeline evaluates model quality against thresholds and automatically deploys passing models to a serving endpoint with monitoring.