Designing Machine Learning Systems | Chapter 6: Model Development & Offline Evaluation
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Read the detailed version on: https://onepagecode.substack.com/ Use this url: https://onepagecode.substack.com/p/large-language-models-architectures Get the book on amazon here: https://amzn.to/3T5SBTV In Chapter 6 of "Designing Machine Learning Systems" by Chip Huyen, we move into the core of model development — selecting, training, and evaluating machine learning models for production. This chapter provides practical guidance on how to choose the right ML algorithms based on your constraints, data, and business goals. We cover six key tips for model selection, including avoiding the state-of-the-art trap, starting simple, and understanding model assumptions. We then explore ensemble methods (bagging, boosting, and stacking), which remain one of the most reliable ways to boost model performance in both competitions and production. The chapter also covers essential practices like experiment tracking, versioning, and debugging ML models — skills that separate junior from senior ML engineers. For large-scale training, we discuss distributed training techniques including data parallelism, model parallelism, and pipeline parallelism. We also introduce AutoML, covering hyperparameter tuning, neural architecture search, and learned optimizers. Finally, we dive deep into offline model evaluation beyond simple accuracy — including perturbation tests, invariance tests, model calibration, confidence measurement, and slice-based evaluation. What you’ll learn in this chapter: • Six practical tips for selecting the right ML model • Bagging, boosting, and stacking ensemble techniques • Experiment tracking and versioning best practices • How to debug ML models effectively • Data, model, and pipeline parallelism for distributed training • AutoML and hyperparameter tuning • Advanced offline evaluation methods (calibration, slice-based eval, invariance tests) This chapter bridges the gap between academic model development and real-world production requirements. #DesigningM
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