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# 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-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 autoscaling
   - KEDA for event-driven scaling
   - Istio service mesh for traffic management
   - Model caching and warm-up strategies

3. Storage and data access:
   - PVC strategies for training data
   - Model artifact storage with CSI drivers
   - Distributed storage for feature stores
   - Cache layers for inference optimization

Provide Kubernetes manifests and Helm charts for entire ML platform.
</Task>

## Phase 4: Monitoring & Continuous Improvement

<Task>
subagent_type: observability-engineer
prompt: |
  Implement comprehensive monitoring for ML system deployed in: {phase3.mlops-engineer.output}
  Using Kubernetes infrastructure: {phase3.kubernetes-architect.output}

Monitoring framework:

1. Model performance monitoring:
   - Prediction accuracy tracking
   - Latency and throughput metrics
   - Feature importance shifts
   - Business KPI correlation

2. Data and model drift detection:
   - Statistical drift detection (KS test, PSI)
   - Concept drift monitoring
   - Feature distribution tracking
   - Automated drift alerts and reports

3. System observability:
   - Prometheus metrics for all components
   - Grafana dashboards for visualization
   - Distributed tracing with Jaeger/Zipkin
   - Log aggregation with ELK/Loki

4. Alerting and automation:
   - PagerDuty/Opsgenie integration
   - Automated retraining triggers
   - Performance degradation workflows
   - Incident response runbooks

5. Cost tracking:
   - Resource utilization metrics
   - Cost allocation by model/experiment
   - Optimization recommendations
   - Budget alerts and controls

Deliver monitoring configuration, dashboards, and alert rules.
</Task>

## Configuration Options

- **experiment_tracking**: mlflow | wandb | neptune | clearml
- **feature_store**: feast | tecton | databricks | custom
- **serving_platform**: kserve | seldon | torchserve | triton
- **orchestration**: kubeflow | airflow | prefect | dagster
- **cloud_provider**: aws | azure | gcp | multi-cloud
- **deployment_mode**: realtime | batch | streaming | hybrid
- **monitoring_stack**: prometheus | datadog | newrelic | custom

## Success Criteria

1. **Data Pipeline Success**:
   - < 0.1% data quality issues in production
   - Automated data validation passing 99.9% of time
   - Complete data lineage tracking
   - Sub-second feature serving latency

2. **Model Performance**:
   - Meeting or exceeding baseline metrics
   - < 5% performance degradation before retraining
   - Successful A/B tests with statistical significance
   - No undetected model drift > 24 hours

3. **Operational Excellence**:
   - 99.9% uptime for model serving
   - < 200ms p99 inference latency
   - Automated rollback within 5 minutes
   - Complete observability with < 1 minute alert time

4. **Development Velocity**:
   - < 1 hour from commit to production
   - Parallel experiment execution
   - Reproducible training runs
   - Self-service model deployment

5. **Cost Efficiency**:
   - < 20% infrastructure waste
   - Optimized resource allocation
   - Automatic scaling based on load
   - Spot instance utilization > 60%

## Final Deliverables

Upon completion, the orchestrated pipeline will provide:

- End-to-end ML pipeline with full automation
- Comprehensive documentation and runbooks
- Production-ready infrastructure as code
- Complete monitoring and alerting system
- CI/CD pipelines for continuous improvement
- Cost optimization and scaling strategies
- Disaster recovery and rollback procedures
