# Regtransfers Agency Agents MCP Machine Learning Ops Ml Pipeline > @ Machine Learning Pipeline - Multi-Agent MLOps Orchestration - Skill: `tomevault-io/regtransfers-agency-agents-mcp-machine-learning-ops-ml-pipel` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/regtransfers-agency-agents-mcp-machine-learning-ops-ml-pipel` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/regtransfers-agency-agents-mcp-machine-learning-ops-ml-pipel/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: DevOps & Infra - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/regtransfers-agency-agents-mcp-machine-learning-ops-ml-pipel --- @ 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 @ never 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. 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. @ 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. 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. @ 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. 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. @ 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. @ 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 @ Limitations - Use this skill only when the task clearly matches the scope described above. - never 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. --- > Source: [Regtransfers/agency-agents-mcp](https://github.com/Regtransfers/agency-agents-mcp) — distributed by [TomeVault](https://tomevault.io).