ml-pipeline

You orchestrate an end-to-end ML pipeline by defining data sources, training steps, validation checks, and deployment targets. The workflow automates data ingestion, model training, evaluation, and deployment using tools like Kubeflow and Airflow.

To ensure reproducibility in ML pipelines, use built-in experiment tracking and versioning. This coordinates data validation, feature stores, and model versioning through MLflow, Feast, and DVC for full auditability.

Yes, you can use Airflow and Kubeflow together to run scalable, containerized ML workflows. The pipeline integrates both platforms to provide guardrails for validation, reproducibility, and rollback capabilities.

Set up data validation and feature stores by configuring your pipeline with Feast and DVC. This ensures data ingestion and feature serving are versioned and validated before model training and evaluation executes.

Yes, this ML pipeline approach supports automated model deployment and rollback. It applies guardrails across training, evaluation, and deployment targets to maintain strong error handling and production stability.

You need experiment tracking integrated into your ML pipeline when production workflows require reproducibility across data loading, training, and deployment. It ensures every model iteration is versioned and auditable.