machine-learning-ops-ml-pipelinelisted

# 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 retraining, A/B testing, and drift detection The multi-agent approach ensures each aspect is handled by domai