machine-learning-ops-ml-pipeline

Design and implement a complete ML pipeline for: $ARGUMENTS

.agents/skills/machine-learning-ops-ml-pipeline Python

3+ layers Tracked stack

Architectural Overview

Skill Reading

"This module is grounded in security patterns and exposes 1 core capabilities across 1 execution phases."

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:

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. </Task>

subagent_type: data-scientist prompt: | Design feature engineering and model requirements for: $ARGUMENTS Using data architecture from: {phase1.data-engineer.output}

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. </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:

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. </Task>

subagent_type: python-pro prompt: | Optimize and productionize ML code from: {phase2.ml-engineer.output}

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. </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:

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. </Task>

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

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. </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:

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. </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

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%