Machine Learning Design Patterns

with Valliappa (Lak) Lakshmanan and Sara Robinson

The design patterns in this book capture best practices and solutions to recurring problems in machine learning. We draw on our experience working as an AI Engineer in Google Cloud and the learned experiences of our colleagues to 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.

In this book, we cover detailed explanations of 30 design patterns from data and problem representation, operationalization, repeatability and reproducibility to 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.

All book sales profits are donated to Girls Who Code

Explainable AI for Practitioners: Designing and Implementing Explainable ML Solutions

with David Pitman, foreward by Ankur Taly

Most intermediate-level machine learning books focus on how to optimize models by increasing accuracy or decreasing prediction error. But this approach often overlooks the importance of understanding why and how your ML model makes the predictions that it does.

Explainability methods provide an essential toolkit for better understanding model behavior, and this practical guide brings together best-in-class techniques for model explainability. This book is meant to bridge the gap between the ever evolving research landscape of interpretable and explainable AI techniques and the real-world best practices for applying these tools in your ML solutions. Experienced machine learning engineers and data scientists will learn hands-on how these techniques work so that you’ll be able to apply these tools more easily in your daily workflow.

All book sales profits are donated to The Sentencing Project