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.