Chapter 14: MLOps & Production Machine Learning Systems

Running ML in production: system design, feature stores and pipelines, model optimization, inference serving, and governed self-improvement.

• End-to-End ML System Design: Training, Validation, Feature Engineering, and Inference Pipelines
• Feature Stores, Dataset Versioning (DVC), and Pipeline Orchestration (Airflow, Kubeflow)
• Model Optimization: Quantization (INT8/FP16), Pruning, Knowledge Distillation, and Model Compilation (TensorRT, ONNX)
• High-Performance Inference: Batching, Parallel Execution, and Real-Time vs Async Pipeline Serving
• Self-Improving Machine Learning Systems: Feedback Loops, Active Learning, Online Recalibration, Continuous Drift Detection, and Auto-Tuning Pipelines
• Chapter 14 References