mlops-standards

What problem does it solve?

This Skill establishes and enforces production-grade standards for Machine Learning systems, ensuring reproducibility, robust tracking, and reliable deployment.

Core Features & Use Cases

• MLOps Maturity Assessment: Evaluate current ML practices against a defined maturity model.
• Reproducibility Guidelines: Provides concrete steps for seed management, config externalization, and environment pinning.
• Experiment Tracking Best Practices: Details what to log, naming conventions, and tool recommendations (W&B vs. MLflow).
• Code Quality & Documentation: Enforces Python style, documentation standards, and error handling.
• Deployment Checklist: A comprehensive audit for models before production release.
• CI/CD Pipeline Standards: Outlines minimum requirements for automated integration and deployment.

Quick Start

Use the mlops-standards skill to review the reproducibility standards for an ML project.