SKILL.md
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.
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 retraining, A/B testing, and drift detection
The multi-agent approach ensures each aspect is handled by domain experts:
- Data engineers handle ingestion and quality
- Data scientists design features and experiments
- ML engineers implement training pipelines
- MLOps engineers handle production deployment
- Observability engineers ensure monitoring