machine-learning-ops-ml-pipeline

Machine Learning Pipeline - Multi-Agent MLOps Orchestration

Design and implement a complete ML pipeline for: $ARGUMENTS

Use this skill when

• Working on machine learning pipeline - multi-agent mlops orchestration tasks or workflows
• Needing guidance, best practices, or checklists for machine learning pipeline - multi-agent mlops orchestration

Do not use this skill when

• The task is unrelated to machine learning pipeline - multi-agent mlops orchestration
• You need a different domain or tool outside this scope

Instructions

• Clarify goals, constraints, and required inputs.
• Apply relevant best practices and validate outcomes.
• Provide actionable steps and verification.
• If detailed examples are required, open resources/implementation-playbook.md.

Thinking

This workflow orchestrates multiple specialized agents to build a production-ready ML pipeline following modern MLOps best practices. The approach emphasizes:

• Phase-based coordination: Each phase builds upon previous outputs, with clear handoffs between agents
• Modern tooling integration: MLflow/W&B for experiments, Feast/Tecton for features, KServe/Seldon for serving
• Production-first mindset: Every component designed for scale, monitoring, and reliability
• Reproducibility: Version control for data, models, and infrastructure
• Continuous improvement: Automated retrainin