
Автор: Patrick Hall, James Curtis, Parul Pandey
Издательство: O’Reilly Media, Inc.
Год: 2023-01-11
Страниц: 350
Язык: английский
Формат: epub
Размер: 21.3 MB
Today, Machine Learning (ML) is the most commercially viable sub-discipline of Artificial Ontelligence (AI). ML systems are used to make high-risk decisions in employment, bail, parole, lending, security and in many other high-impact applications throughout the world’s economies and governments. In a corporate setting, ML systems are used in all parts of an organization — from consumer-facing products, to employee assessments, to back-office automation, and more. Indeed, the past decade has brought with it even wider adoption of ML technologies. But it has also proven that ML presents risks to it’s operators, consumers, and even the general public. Machine Learning for High-Risk Applications will arm practitioners with a solid understanding of model risk management processes and new ways to use common Python tools for training explainable models and debugging them for reliability, safety, bias management, security and privacy issues.