Explainable AI for Practitioners
Designing and Implementing Explainable ML Solutions
Published on 11. November 2022
Book
Paperback/Softback
250 pages
978-1-0981-1913-3 (ISBN)
Description
Most intermediate-level machine learning books usually focus on how to optimize models by increasing accuracy or decreasing prediction error. But this approach often overlooks the importance and the need to be able to explain why and how your ML model makes the predictions that it does.
This practical guide brings together the best-in-class techniques for model interpretability and explains model predictions in a hands-on approach. Experienced ML practitioners will be able to more easily apply these tools in their daily workflow.
This practical guide brings together the best-in-class techniques for model interpretability and explains model predictions in a hands-on approach. Experienced ML practitioners will be able to more easily apply these tools in their daily workflow.
More details
Language
English
Place of publication
Sebastopol
United States
Target group
Professional and scholarly
Product notice
Paperback (trade)
Unsewn / adhesive bound
Dimensions
Height: 232 mm
Width: 178 mm
Thickness: 17 mm
Weight
484 gr
ISBN-13
978-1-0981-1913-3 (9781098119133)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Michael Munn | David Pitman
Explainable AI for Practitioners
Designing and Implementing Explainable ML Solutions
E-Book
10/2022
O'Reilly
€58.99
Available for download
Michael Munn | David Pitman
Explainable AI for Practitioners
E-Book
10/2022
O'Reilly
€58.99
Available for download
Persons
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.