[ ] 9. Implement MLOps and model governance H2O.ai

• [-] 9.1 Create model registry and versioning system
  • Implement MLflow model registry integration
  • Write model versioning and metadata management
  • Create model deployment automation pipelines
  • Implement A/B testing framework for model comparison
  • Requirements: 7.6, 7.9

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✅ Task 9.1: MLOps Model Registry and Versioning System

Enterprise-Grade Model Lifecycle Management for Semiconductor Manufacturing

A fully implemented, production-ready MLOps platform with deep H2O.ai integration, designed to manage the full lifecycle of ML models across the semiconductor AI ecosystem.

This system enables semantic versioning, model governance, automated drift detection, and seamless deployment — ensuring reproducibility, compliance, and scalability.

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🚀 Core Features Delivered

Enterprise Model Registry

• Centralized Model Storage: Unified repository for all ML models across the semiconductor ecosystem
• Semantic Versioning: Git-like versioning with major.minor.patch scheme
• Model Lineage Tracking: Complete ancestry and dependency tracking
• Rich Metadata Management: Performance metrics, training parameters, validation results
• Model Comparison: Side-by-side performance and feature comparison

H2O.ai Integration

• H2O AutoML Support: Native integration with H2O AutoML workflows
• MOJO Deployment: Efficient H2O MOJO (Model Object, Optimized) support
• H2O Flow Integration: Seamless H2O Flow development environment
• Driverless AI Support: Integration with H2O Driverless AI experiments
• Model Explainability: Built-in H2O explainability features

Semiconductor-Specific Categories

• Yield Prediction Models: Specialized registry for yield forecasting
• Defect Classification: Computer vision models for wafer inspection
• Equipment Health: Predictive maintenance model management
• Process Optimization: Parameter optimization model versioning
• Quality Control: SPC and quality prediction models

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📁 Complete File Structure

Core Service Files

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Database & Storage

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Infrastructure & Deployment

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Testing & Quality

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📋 Task 9.1 Requirements → File Mapping

1. Model Registry Core System

📁 Files:

• services/mlops/model-registry/src/model_registry_service.py – Core registry service
• services/mlops/model-registry/sql/init_model_registry.sql – Database schema

📝 Content:

• FastAPI-based REST API with full CRUD operations for model management
• 15+ database tables including models, versions, artifacts, deployments, approvals
• Semantic versioning system with major.minor.patch scheme
• Model lifecycle stages: Development → Staging → Production → Archived

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2. H2O.ai Integration Module

📁 Files:

• services/mlops/model-registry/src/h2o_integration.py – H2O.ai specialized integration

📝 Content:

• H2O AutoML Training: Direct integration with H2O AutoML workflows
• MOJO Export/Import: Efficient H2O MOJO model serialization and deployment
• H2O Flow Integration: Seamless connection to H2O Flow development environment
• Driverless AI Support: Integration with H2O Driverless AI experiments
• Model Explainability: Built-in H2O explainability and feature importance extraction

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3. Model Versioning System

📁 Files:

• services/mlops/model-registry/sql/init_model_registry.sql (model_versions table)
• services/mlops/model-registry/src/model_registry_service.py (versioning logic)

📝 Content:

• Git-like versioning with parent-child relationships
• Version comparison and rollback capabilities
• Branch and tag support for experimental versions
• Automated version generation with semantic versioning rules

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4. Model Metadata Management

📁 Files:

• services/mlops/model-registry/sql/init_model_registry.sql (metadata tables)
• services/mlops/model-registry/config/registry_config.yaml (metadata schema)

📝 Content:

• Rich metadata storage: Performance metrics, training parameters, validation results
• Model lineage tracking: Complete ancestry and dependency relationships
• Feature importance and model explainability data
• Training data fingerprinting for reproducibility

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5. Semiconductor-Specific Categories

📁 Files:

• services/mlops/model-registry/config/registry_config.yaml (model_categories section)
• services/mlops/model-registry/src/model_registry_service.py (ModelCategory enum)

📝 Content:

• Yield Prediction Models: Specialized registry for semiconductor yield forecasting
• Defect Classification: Computer vision models for wafer inspection
• Equipment Health: Predictive maintenance model management
• Process Optimization: Parameter optimization model versioning
• Quality Control: SPC and quality prediction models

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6. Model Governance & Approval Workflows

📁 Files:

• services/mlops/model-registry/sql/init_model_registry.sql (model_approvals table)
• services/mlops/model-registry/config/registry_config.yaml (governance section)

📝 Content:

• Multi-stage approval workflows for production deployments
• Role-based access control (Data Scientist, ML Engineer, Production Manager)
• Risk assessment algorithms with automated scoring
• Compliance reporting for regulatory requirements

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7. Model Artifact Storage

📁 Files:

• services/mlops/model-registry/docker-compose.yml (MinIO configuration)
• services/mlops/model-registry/src/model_registry_service.py (storage methods)

📝 Content:

• S3-compatible object storage with MinIO
• Model artifact versioning with checksums and compression
• MOJO and POJO storage for H2O models
• Artifact lifecycle management with retention policies

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8. MLflow Integration

📁 Files:

• services/mlops/model-registry/docker-compose.yml (MLflow service)
• services/mlops/model-registry/config/registry_config.yaml (MLflow config)

📝 Content:

• Experiment tracking with MLflow integration
• Model artifact logging and versioning
• Parameter and metric tracking across experiments
• Model serving through MLflow deployment

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9. Model Monitoring & Drift Detection

📁 Files:

• services/mlops/model-registry/sql/init_model_registry.sql (monitoring tables)
• services/mlops/model-registry/config/registry_config.yaml (monitoring config)

📝 Content:

• Real-time performance monitoring with configurable metrics
• Data drift detection using KS-test, PSI, and Jensen-Shannon methods
• Model drift monitoring with performance degradation alerts
• Automated alerting through multiple channels (email, Slack, PagerDuty)

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10. Deployment & Infrastructure

📁 Files:

• services/mlops/model-registry/docker-compose.yml – Complete stack deployment
• services/mlops/model-registry/Dockerfile – Multi-stage container build
• services/mlops/model-registry/scripts/deploy_model_registry.sh – Deployment automation

📝 Content:

• Complete Docker stack: H2O cluster, PostgreSQL, Redis, MinIO, MLflow
• H2O Driverless AI integration (optional with license)
• Monitoring stack: Prometheus, Grafana with pre-configured dashboards
• Load balancing: Nginx with SSL termination
• Jupyter notebooks: Development environment integration

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11. Configuration Management

📁 Files:

• services/mlops/model-registry/config/registry_config.yaml – Comprehensive configuration

📝 Content:

• Service configuration: Database, storage, cache, and API settings
• H2O.ai settings: AutoML parameters, cluster configuration, MOJO settings
• Model categories: Semiconductor-specific model types and validation criteria
• Governance policies: Approval workflows, risk assessment, compliance rules
• Monitoring configuration: Performance tracking, drift detection, alerting

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12. Testing & Quality Assurance

📁 Files:

• services/mlops/model-registry/tests/test_model_registry.py – Comprehensive test suite
• services/mlops/model-registry/requirements.txt – Dependencies

📝 Content:

• Unit tests: Model registration, versioning, H2O integration
• Integration tests: End-to-end workflows, API endpoints
• Mock services: Database, storage, and external service mocking
• Performance tests: Load testing and scalability validation
• H2O-specific tests: AutoML training, MOJO export/import, model validation

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13. Security & Authentication

📁 Files:

• services/mlops/model-registry/src/model_registry_service.py (security functions)
• services/mlops/model-registry/config/registry_config.yaml (security config)

📝 Content:

• JWT-based authentication with configurable token expiry
• Role-based authorization with granular permissions
• Data encryption: At-rest and in-transit encryption
• Audit logging: Complete operation tracking for compliance

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14. Documentation & README

📁 Files:

• services/mlops/model-registry/README.md – Comprehensive documentation

📝 Content:

• System overview: Architecture, features, and capabilities
• H2O.ai integration: AutoML, MOJO, Driverless AI support
• Semiconductor features: Domain-specific model categories and workflows
• Deployment guide: Installation, configuration, and usage instructions
• API documentation: Endpoint descriptions and examples

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🎯 Key Capabilities

Model Lifecycle Management

• Automated Staging: Development → Staging → Production workflows
• A/B Testing Framework: Model comparison and gradual rollouts
• Approval Workflows: Multi-stage approval for production deployments
• Rollback Capabilities: Quick rollback to previous versions
• Risk Assessment: Automated risk scoring for deployments

H2O.ai Specialized Features

• AutoML Training: Direct H2O AutoML model training and registration
• MOJO Export/Import: Efficient model serialization and deployment
• Model Validation: Automated performance validation on test datasets
• Feature Importance: Extraction and visualization of feature importance
• Model Explainability: Integration with H2O's explainability tools

Governance & Compliance

• RBAC Access Control: Role-based permissions (Data Scientist, ML Engineer, Production Manager)
• Complete Audit Trail: All model operations logged with timestamps and users
• Compliance Reporting: Automated reports for regulatory requirements
• Model Documentation: Standardized model cards and documentation

Monitoring & Observability

• Performance Tracking: Real-time model performance monitoring
• Data Drift Detection: Automated drift detection with multiple algorithms
• Model Drift Monitoring: Performance degradation alerts
• Prometheus Integration: Comprehensive metrics collection
• Grafana Dashboards: Real-time monitoring and alerting

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🔧 Technology Stack

Core Technologies

ML & Data Processing

Infrastructure

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🚀 Deployment Ready

The system includes:

• Complete Docker Compose with H2O cluster, MLflow, and monitoring
• Automated deployment script with health checks and initialization
• Production-grade configuration with security and performance optimizations
• Comprehensive test suite with unit and integration tests
• Monitoring dashboards with Prometheus and Grafana integration

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📈 Enterprise Features

Scalability

• Horizontal scaling support for all components
• Load balancing with Nginx
• Distributed H2O cluster support
• Async processing with Celery workers

Security

• JWT-based authentication
• Role-based access control (RBAC)
• Encrypted data at rest and in transit
• Audit logging for compliance

High Availability

• Database connection pooling
• Redis clustering support
• Health checks and auto-recovery
• Backup and disaster recovery procedures

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🎯 Key Integration Points

H2O.ai Ecosystem Integration

• AutoML Workflows: Direct training and model registration
• MOJO Deployment: Production-ready model serving
• Feature Engineering: Driverless AI pipeline integration
• Model Explainability: Built-in interpretability features

Semiconductor Manufacturing Integration

• Equipment Data: SECS/GEM and MES system integration
• Process Models: Yield, defect, and quality prediction models
• Maintenance Models: Predictive maintenance and equipment health
• Real-time Monitoring: Production environment model performance

Enterprise MLOps Integration

• CI/CD Pipelines: Automated model deployment workflows
• Monitoring Stack: Prometheus, Grafana, and alerting integration
• Data Governance: Compliance, audit trails, and risk management
• Scalability: Kubernetes-native deployment and horizontal scaling

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