god-mlops-core

What problem does it solve?

End-to-end MLOps requires rigorous reproducibility, governance, and scalable deployment. This skill codifies a battle-tested workflow for designing ML pipelines, versioning data, tracking experiments, training at scale, validating models, and deploying with robust monitoring.

Core Features & Use Cases

• End-to-end ML pipeline design, data versioning, experiment tracking, model training at scale, and deployment workflows.
• Integration-ready with MLflow, Kubeflow, SageMaker, Vertex AI, DVC, Feast, BentoML, and Triton to orchestrate production ML systems.
• Use Case: teams shipping models to production with auditable experiments, drift monitoring, and automated retraining triggers.

Quick Start

Load the god-mlops-core skill and start building a production-grade ML pipeline with reproducible experiments and monitored deployments.