Explainable AI for Practitioners : Designing and Implementing Explainable ML Solutions

Michael Munn

ISBN 10: 1098119134 / ISBN 13: 9781098119133

Published by O'reilly Media Dez 2022, 2022

About this Item

Neuware - 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.

Seller Inventory # 9781098119133

Bibliographic Details

• Title: Explainable AI for Practitioners : Designing and Implementing Explainable ML Solutions
• Publisher: O'reilly Media Dez 2022
• Publication Date: 2022
• Language: English
• ISBN 10: 1098119134
• ISBN 13: 9781098119133
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 232W x 178H x 17D millimeters 232 millimeters width by 178 millimeters height by 17 millimeters depth
• Weight: 484 grams

About this title

Synopsis

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. 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.

This essential book provides:

• A detailed look at some of the most useful and commonly used explainability techniques, highlighting pros and cons to help you choose the best tool for your needs
• Tips and best practices for implementing these techniques
• A guide to interacting with explainability and how to avoid common pitfalls
• The knowledge you need to incorporate explainability in your ML workflow to help build more robust ML systems
• Advice about explainable AI techniques, including how to apply techniques to models that consume tabular, image, or text data
• Example implementation code in Python using well-known explainability libraries for models built in Keras and TensorFlow 2.0, PyTorch, and HuggingFace

About the Authors

Michael Munn is a research software engineer at Google. His work focuses on better understanding the mathematical foundations of machine learning and how those insights can be used to improve machine learning models at Google. Previously, he worked in the Google Cloud Advanced Solutions Lab helping customers design, implement, and deploy machine learning models at scale. Michael has a PhD in mathematics from the City University of New York. Before joining Google, he worked as a research professor.

David Pitman is a staff engineer working in Google Cloud on the AI Platform, where he leads the Explainable AI team. He's also a co-organizer of PuPPy, the largest Python group in the Pacific Northwest. David has a Masters of Engineering degree and a BS in computer science from MIT, where he previously served as a research scientist.