data-ai-ml-pipeline

What problem does it solve?

End-to-end ML pipelines across data ingestion, feature engineering, training orchestration, experiment tracking, validation, and deployment automation become reproducible and maintainable.

Core Features & Use Cases

• End-to-end pipeline design and orchestration across stages (data, features, training, evaluation, deployment)
• Feature store integration and training workflows
• Experiment tracking, model registry, and deployment automation
• Real-world use case: coordinate data pipelines, experiments, and model deployment across Kubeflow, MLflow, and Feast in production

Quick Start

Configure a reproducible ML pipeline by specifying data sources, feature stores, training steps, and deployment targets, then run the end-to-end workflow.