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ml-design-patterns

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!

Chapters

• 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