Agent skill · product strategy · aiskillstore
machine-learning-ops-ml-pipeline
Design and implement a complete ML pipeline for: $ARGUMENTS
Why this skill is useful
Adds detailed workflows and implementation code for orchestrating multi-agent MLOps pipelines that aren't covered in standard documentation.
What it needs
Requires dvc, feast, horovod, kserve, lakefs, mlflow, optuna, pytorch, ray, seldon, tecton, wandb installed locally. About 5k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
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 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 Phase 1: Data & Requirements Analysis <Task> subagenttype: data-engineer prompt: Analyze and design data pipeline for ML system with requirements: $ARGUMENTS Deliverables: 1. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/machine-learning-ops-ml-pipeline