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Source code accompanying O'Reilly book:
Title: Machine Learning Design Patterns
Authors: Valliappa (Lak) Lakshmanan, Sara Robinson, Michael Munn
https://www.oreilly.com/library/view/machine-learning-design/9781098115777/
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We will update this repo with source code as we write each chapter. Stay tuned!
- Preface
- The Need for ML Design Patterns
- Data representation design patterns
- #1 Hashed Feature
- #2 Embedding
- #3 Feature Cross
- #4 Multimodal Input
- Problem representation design patterns
- #5 Reframing
- #6 Multilabel
- #7 Ensemble
- #8 Cascade
- #9 Neutral Class
- #10 Rebalancing
- Patterns that modify model training
- #11 Useful overfitting
- #12 Checkpoints
- #13 Transfer Learning
- #14 Distribution Strategy
- #15 Hyperparameter Tuning
- Resilience patterns
- #16 Stateless Serving Function
- #17 Batch Serving
- #18 Continuous Model Evaluation
- #19 Two Phase Predictions
- #20 Keyed Predictions
- Reproducibility patterns
- #21 Transform
- #22 Repeatable Sampling
- #23 Bridged Schema
- #24 Windowed Inference
- #25 Workflow Pipeline
- #26 Feature Store
- #27 Model Versioning
- Responsible AI
- #28 Heuristic benchmark
- #29 Explainable Predictions
- #30 Fairness Lens
- Summary
