Machine Learning Ops Ml Pipeline

This orchestrates a full production ML pipeline using coordinated specialist agents for each phase: data engineering, feature design, model training, deployment, and monitoring. You get phase-based workflows where a data engineer sets up ingestion and quality checks, a data scientist designs features and experiments, an ML engineer builds training with MLflow or W&B, and DevOps handles Kubernetes deployment with KServe or Seldon. It's comprehensive but heavy, clearly aimed at teams building serious MLOps infrastructure rather than quick experiments. The multi-agent handoff approach makes sense for complex pipelines where you want domain expertise at each layer, though you'll need to adapt the templates to your actual stack and requirements.

Install to Claude Code

npx -y skills add sickn33/antigravity-awesome-skills --skill machine-learning-ops-ml-pipeline --agent claude-code