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- Complete MLOps pipeline with 4 core components: 1. ModelMonitor: Tracks performance over time, detects degradation 2. DriftDetector: Statistical tests for data distribution changes 3. RetrainingTrigger: Automated retraining based on thresholds 4. MLOpsPipeline: Orchestrates complete workflow integration - Follows TinyTorch educational pattern exactly: - Concept explanations before implementation - Guided TODOs with step-by-step instructions - Immediate testing after each component - Progressive complexity building on previous modules - Comprehensive summary with career applications - Integrates all previous TinyTorch components: - Uses training pipeline from Module 09 - Uses benchmarking from Module 12 - Uses compression from Module 10 - Demonstrates complete ecosystem integration - Production-ready MLOps concepts: - Performance monitoring and alerting - Drift detection with statistical validation - Automated retraining triggers - Model lifecycle management - Complete deployment workflows - Educational value: - Real-world MLOps applications (Netflix, Uber, Google) - Industry connections (MLflow, Kubeflow, SageMaker) - Career preparation for ML Engineer roles - Complete capstone bringing together all 13 modules - Technical implementation: - 1700+ lines of educational content and code - NBGrader integration for assessment - Comprehensive test suite with 100+ points - Auto-discovery testing framework - Professional documentation and examples This completes the TinyTorch ecosystem with production-ready MLOps