Explainable AI for Practitioners

Designing and Implementing Explainable ML Solutions

Michaël Munn, David Pitman

250 pages, parution le 30/12/2022

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Résumé

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.

L'auteur - Michaël Munn

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

L'auteur - David Pitman

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.

Caractéristiques techniques

	PAPIER
Éditeur(s)	O'Reilly
Auteur(s)	Michaël Munn, David Pitman
Parution	30/12/2022
Nb. de pages	250
Couverture	Broché
Intérieur	Noir et Blanc
EAN13	9781098119133