machine-learning-ops-ml-pipeline

name	machine-learning-ops-ml-pipeline
description	Design and implement a complete ML pipeline for: $ARGUMENTS
type	skill
created	2026-02-27T00:00:00.000Z
domain	data
category	data-engineering
risk	unknown
source	community
tags	["skill","data","data-engineering","machine","learning","ops"]

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

subagent_type: data-engineer prompt: | Analyze and design data pipeline for ML system with requirements: $ARGUMENTS

Deliverables:

1. Data source audit and ingestion strategy:

   • Source systems and connection patterns
   • Schema validation using Pydantic/Great Expectations
   • Data versioning with DVC or lakeFS
   • Incremental loading and CDC strategies

2. Data quality framework:

   • Profiling and statistics generation

subagent_type: data-scientist prompt: | Design feature engineering and model requirements for: $ARGUMENTS Using data architecture from: {phase1.data-engineer.output} subagent_type: ml-engineer prompt: | Implement training pipeline based on requirements: {phase1.data-scientist.output} Using data pipeline: {phase1.data-engineer.output} subagent_type: python-pro prompt: | Optimize and productionize ML code from: {phase2.ml-engineer.output} subagent_type: mlops-engineer prompt: | Design production deployment for models from: {phase2.ml-engineer.output} With optimized code from: {phase2.python-pro.output} subagent_type: kubernetes-architect prompt: | Design Kubernetes infrastructure for ML workloads from: {phase3.mlops-engineer.output} subagent_type: observability-engineer prompt: | Implement comprehensive monitoring for ML system deployed in: {phase3.mlops-engineer.output} Using Kubernetes infrastructure: {phase3.kubernetes-architect.output}