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 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> 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
   • Anomaly detection rules
   • Data lineage tracking
   • Quality gates and SLAs

3. Storage architecture:

   • Raw/processed/feature layers
   • Partitioning strategy
   • Retention policies
   • Cost optimization

Provide implementation code for critical components and integration patterns. </Task>

<Task> subagent_type: data-scientist prompt: | Design feature engineering and model requirements for: $ARGUMENTS Using data architecture from: {phase1.data-engineer.output}

Deliverables:

1. Feature engineering pipeline:

   • Transformation specifications
   • Feature store schema (Feast/Tecton)
   • Statistical validation rules
   • Handling strategies for missing data/outliers

2. Model requirements:

   • Algorithm selection rationale
   • Performance metrics and baselines
   • Training data requirements
   • Evaluation criteria and thresholds

3. Experiment design:

   • Hypothesis and success metrics
   • A/B testing methodology
   • Sample size calculations
   • Bias detection approach

Include feature transformation code and statistical validation logic. </Task>

Phase 2: Model Development & Training

<Task> subagent_type: ml-engineer prompt: | Implement training pipeline based on requirements: {phase1.data-scientist.output} Using data pipeline: {phase1.data-engineer.output}

Build comprehensive training system:

1. Training pipeline implementation:

   • Modular training code with clear interfaces
   • Hyperparameter optimization (Optuna/Ray Tune)
   • Distributed training support (Horovod/PyTorch DDP)
   • Cross-validation and ensemble strategies

2. Experiment tracking setup:

   • MLflow/Weights & Biases integration
   • Metric logging and visualization
   • Artifact management (models, plots, data samples)
   • Experiment comparison and analysis tools

3. Model registry integration:

   • Version control and tagging strategy
   • Model metadata and lineage
   • Promotion workflows (dev -> staging -> prod)
   • Rollback procedures

Provide complete training code with configuration management. </Task>

<Task> subagent_type: python-pro prompt: | Optimize and productionize ML code from: {phase2.ml-engineer.output}

Focus areas:

1. Code quality and structure:

   • Refactor for production standards
   • Add comprehensive error handling
   • Implement proper logging with structured formats
   • Create reusable components and utilities

2. Performance optimization:

   • Profile and optimize bottlenecks
   • Implement caching strategies
   • Optimize data loading and preprocessing
   • Memory management for large-scale training

3. Testing framework:

   • Unit tests for data transformations