Chapter 14 References

Books

• Designing Machine Learning Systems by Chip Huyen, O’Reilly Media, 2022.
• Building Machine Learning Powered Applications by Emmanuel Ameisen, O’Reilly Media, 2020.
• Machine Learning Engineering in Action by Ben Wilson, Manning Publications, 2022.

Websites

• DVC User Guide — data and pipeline versioning, pipeline definitions, and reproducibility.
• Apache Airflow Best Practices — DAG design, testing, deployment, and operational practices.
• Kubeflow Pipelines — versioned pipeline components, execution order, artifacts, caching, and retries.
• ONNX Runtime Documentation — cross-platform execution, hardware execution providers, and model deployment.
• NVIDIA TensorRT Documentation — graph and kernel optimization, precision, and target-device deployment.
• NVIDIA Triton Inference Server User Guide — model repositories, backends, metrics, and inference-server configuration.
• Evidently Data Drift Documentation — reference-versus-current data drift metrics, methods, and thresholds.
• River Active Learning — online active-learning strategies and production labeling considerations.
• scikit-learn Probability Calibration — Platt scaling, isotonic regression, temperature scaling, and calibration evaluation.
• MLflow Model Registry — experiment lineage, model versions, metadata, aliases, and rollback-oriented lifecycle management.