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 Deliverables: 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 Data quality framework: Profiling and statistics generation Anomaly detection rules Data lineage tracking Quality gates and SLAs Storage architecture: Raw/processed/feature layers Partitioning strategy Retention policies Cost optimization Provide implementation code for critical components and integration patterns. Deliverables: Feature engineering pipeline: Transformation specifications Feature store schema (Feast/Tecton) Statistical validation rules Handling strategies for missing data/outliers Model requirements: Algorithm selection rationale Performance metrics and baselines Training data requirements Evaluation criteria and thresholds Experiment design: Hypothesis and success metrics A/B testing methodology Sample size calculations Bias detection approach Include feature transformation code and statistical validation logic. Phase 2: Model Development & Training Build comprehensive training system: 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 Experiment tracking setup: MLflow/Weights & Biases integration Metric logging and visualization Artifact management (models, plots, data samples) Experiment comparison and analysis tools 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. Focus areas: Code quality and structure: Refactor for production standards Add comprehensive error handling Implement proper logging with structured formats Create reusable components and utilities Performance optimization: Profile and optimize bottlenecks Implement caching strategies Optimize data loading and preprocessing Memory management for large-scale training 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. Phase 3: Production Deployment & Serving Implementation requirements: 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) Deployment strategies: Blue-green deployments for zero downtime Canary releases with traffic splitting Shadow deployments for validation A/B testing infrastructure CI/CD pipeline: GitHub Actions/GitLab CI workflows Automated testing gates Model validation before deployment ArgoCD for GitOps deployment 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. Kubernetes-specific requirements: Workload orchestration: Training job scheduling with Kubeflow GPU resource allocation and sharing Spot/preemptible instance integration Priority classes and resource quotas Serving infrastructure: HPA/VPA for autoscaling KEDA for event-driven scaling Istio service mesh for traffic management Model caching and warm-up strategies 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. Phase 4: Monitoring & Continuous Improvement Monitoring framework: Model performance monitoring: Prediction accuracy tracking Latency and throughput metrics Feature importance shifts Business KPI correlation Data and model drift detection: Statistical drift detection (KS test, PSI) Concept drift monitoring Feature distribution tracking Automated drift alerts and reports System observability: Prometheus metrics for all components Grafana dashboards for visualization Distributed tracing with Jaeger/Zipkin Log aggregation with ELK/Loki Alerting and automation: PagerDuty/Opsgenie integration Automated retraining triggers Performance degradation workflows Incident response runbooks Cost tracking: Resource utilization metrics Cost allocation by model/experiment Optimization recommendations Budget alerts and controls Deliver monitoring configuration, dashboards, and alert rules. 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 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 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 Operational Excellence: 99.9% uptime for model serving < 200ms p99 inference latency Automated rollback within 5 minutes Complete observability with < 1 minute alert time Development Velocity: < 1 hour from commit to production Parallel experiment execution Reproducible training runs Self-service model deployment 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 Limitations Use this skill only when the task clearly matches the scope described above. Do not treat the output as a substitute for environment-specific validation, testing, or expert review. Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
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