ml-pipeline-workflow

End-to-end ML pipeline orchestration automates data ingestion, preprocessing, model training, validation, and deployment as a directed acyclic graph (DAG). This Skill supports DAG-based orchestrators like Airflow, Dagster, and Kubeflow to build reproducible workflows with versioning and idempotent steps, ensuring traceability from raw data to production serving.

Yes, this Skill builds pipelines across DAG-based orchestrators including Airflow, Dagster, and Kubeflow. It supports modular pipeline stages, versioning, and idempotent execution across these platforms, letting you choose the orchestrator that fits your infrastructure.

ML pipeline orchestration integrates experiment tracking tools like MLflow and Weights & Biases (W&B) to log training runs, metrics, and model versions alongside pipeline execution. This enables reproducibility, model comparison, and observability across data preparation through deployment stages.

Reproducible ML pipelines require modular stages, versioning of data and models, idempotent steps that produce consistent results on re-run, and deployment patterns with monitoring. This Skill combines orchestration, experiment tracking, and observability to satisfy these production-readiness requirements.

Data preparation and feature engineering are integrated stages within the end-to-end pipeline, executed as part of the DAG between ingestion and training. This Skill supports them as modular, versioned steps alongside training and validation, ensuring consistency and traceability.

After validation, the pipeline moves to deployment, automatically serving the validated model to a production endpoint with monitoring enabled. This Skill handles the transition from training evaluation to serving deployment as a continuous orchestrated workflow.