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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

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Source code accompanying O'Reilly book: Machine Learning Design Patterns

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