Machine Learning Ops ML Pipeline

Machine Learning Pipeline - Multi-Agent MLOps Orchestration

Design and implement a complete ML pipeline for: $ARGUMENTS

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

<Task> 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>

<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

<Task> 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>

<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

<Task> 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>

<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

<Task> 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