https://github.com/GoogleCloudPlatform/ml-design-patterns

Source code accompanying O'Reilly book: Machine Learning Design Patterns
https://github.com/GoogleCloudPlatform/ml-design-patterns

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

• Host: GitHub
• URL: https://github.com/GoogleCloudPlatform/ml-design-patterns
• Owner: GoogleCloudPlatform
• License: apache-2.0
• Archived: true
• Created: 2020-03-17T22:00:06.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2021-04-28T23:11:45.000Z (over 3 years ago)
• Last Synced: 2024-05-15T13:23:06.780Z (6 months ago)
• Language: Jupyter Notebook
• Size: 33.4 MB
• Stars: 1,825
• Watchers: 49
• Forks: 507
• Open Issues: 21
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE

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README

        
*This is not an official Google product*

# 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/  


Buy from O'Reilly 


Buy from Amazon 


We will update this repo with source code as we write each chapter. Stay tuned!

[](https://deepnote.com/launch?url=https://github.com/GoogleCloudPlatform/ml-design-patterns)

# 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