GoogleCloudPlatform/ml-design-patterns resource

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2097 stars · 594 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Jupyter Notebook source code accompanying the O'Reilly book 'Machine Learning Design Patterns' by Lakshmanan, Robinson, and Munn. It demonstrates 30 ML design patterns covering data representation, model training, serving, reproducibility, and responsible AI.

Use cases

• learn machine learning design patterns from a book
• find example notebooks for feature engineering patterns like embeddings and feature crosses
• study best practices for ML model serving and reproducibility
• learn responsible AI techniques like explainability and fairness
• get runnable code examples for hyperparameter tuning and transfer learning

When to choose

• you are reading the Machine Learning Design Patterns book and want the accompanying code
• you want practical notebook examples of common ML engineering patterns
• you are teaching or learning MLOps and responsible AI concepts

When to avoid

• you need a production-ready ML library or framework
• you want actively developed tooling rather than book companion code
• you need patterns beyond the 30 covered in the book

Facets

learning-resource · maturity maintenance

machine-learning data-science developer-tools machine-learning tutorials data-science education python cross-platform design-patterns jupyter-notebooks oreilly-book tensorflow google-cloud mlops

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