machine-learning-ops-ml-pipeline

Machine Learning Pipeline - Multi-Agent MLOps Orchestration

Design and implement a complete ML pipeline for: $ARGUMENTS

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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

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