Book Overview

The design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of experts into straightforward, approachable advice.

What You Will Find

In this book, you will find detailed explanations of 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the best technique for your situation.

Learn How To:

Identify and mitigate common challenges when training, evaluating, and deploying ML models

Represent data for different ML model types, including embeddings, feature crosses, and more

Choose the right model type for specific problems

Build a robust training loop that uses checkpoints, distribution strategy, and hyperparameter tuning

Deploy scalable ML systems that you can retrain and update to reflect new data

Interpret model predictions for stakeholders and ensure models are treating users fairly