--- name: machine-learning-ops-ml-pipeline description: "Design and implement a complete ML pipeline for: $ARGUMENTS" --- # 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 - 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> 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 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> 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 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> 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 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