/ml-pipeline

Machine Learning Pipeline - Multi-Agent MLOps Orchestration

Design and implement a complete ML pipeline for: $ARGUMENTS

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
• Integration tests for pipeline components
• Model quality tests (invariance, directional)
• Performance regression tests

Deliver production-ready, maintainable code with full test coverage. </Task>

Phase 3: Production Deployment & Serving

<Task> subagent_type: mlops-engineer prompt: | Design production deployment for models from: {phase2.ml-engineer.output} With optimized code from: {phase2.python-pro.output}

Implementation requirements:

1. Model serving infrastructure:

• REST/gRPC APIs with FastAPI/TorchServe
• Batch prediction pipelines (Airflow/Kubeflow)
• Stream processing (Kafka/Kinesis integration)
• Model serving platforms (KServe/Seldon Core)

2. Deployment strategies:

• Blue-green deployments for zero downtime
• Canary releases with traffic splitting
• Shadow deployments for validation
• A/B testing infrastructure

3. CI/CD pipeline:

• GitHub Actions/GitLab CI workflows
• Automated testing gates
• Model validation before deployment
• ArgoCD for GitOps deployment

4. Infrastructure as Code:

• Terraform modules for cloud resources
• Helm charts for Kubernetes deployments
• Docker multi-stage builds for optimization
• Secret management with Vault/Secrets Manager

Provide complete deployment configuration and automation scripts. </Task>

<Task> subagent_type: kubernetes-operations-kubernetes-architect prompt: | Design Kubernetes infrastructure for ML workloads from: {phase3.mlops-engineer.output}

Kubernetes-specific requirements:

1. Workload orchestration:

• Training job scheduling with Kubeflow
• GPU resource allocation and sharing
• Spot/preemptible instance integration
• Priority classes and resource quotas

2. Serving infrastructure:

• HPA/VPA for autosc