Preface 1．The Need for Machine Learning Design Patterns What Are Design Patterns? How to Use This Book Machine Learning Terminology Models and Frameworks Data and Feature Engineering The Machine Learning Process Data and Model Tooling Roles Common Chauenges in Machine Learning Data Quality Reproducibility Data Drift Scale Multiple Objectives Summary 2．Data Representation Design Patterns Simple Data Representations Numerical Inputs Categorical Inputs Design Pattern 1： Hashed Feature Problem Solution Why It Works Trade-Offs and Alternatives Design Pattern 2： Embeddings Problem Solution Why It Works Trade-Offs and Alternatives Design Pattern 3： Feature Cross Problem Solution Why It Works Trade-Offs and Alternatives Design Pattern 4： Multimodallnput Problem Solution Trade-Offs and Alternatives Summary 3．Problem Representation Design Patterns Design Pattern 5： Reframing Problem Solution Why It Works Trade-Offs and Alternatives Design Pattern 6： Multilabel Problem Solution Trade-Offs and Alternatives Design Pattern 7： Ensembles Problem Solution Why It Works Trade-Offs and Alternatives Design Pattern 8： Cascade Problem Solution Trade-Offs and Alternatives Design Pattern 9： Neutral Class Problem Solution Why It Works Trade-Offs and Alternatives Design Pattern 10： Re alanang Problem …… 4．ModeI Training Patterns．．． 5．Design Patterns for Resilient Serving 6．Reproduability Design Patterns 7．Responsible AI 8．Connected Patterns Index
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