English | 2021 | ISBN: 9781098108687 | 84 pages | PDF,EPUB | 5.36 MB

Artificial intelligence has the potential to provide productive, efficient, and innovative solutions to everyday problems.

But it comes with risks. Multiple examples of alleged bias in AI have been reported in recent years, and many people were already affected by the those issues surfaced. This could have been avoided if humans had visibility into every stage of the system life cycle.
In this report, Danny Farah and Amit Paka explain the importance of establishing an efficient Model Performance Management (MPM) system in your organizationâs machine learning workflow. Youâll learn how MPM enables CxOs, IT leaders, and AI/ML leaders to gain visibility into every stage of the system life cycle. That includes training ML models to help your system make decisions.
MPM and Explainability: Explore a data-centric framework for producing high-quality ML and AI models and systems
Explainable AI (XAI): Generate explanations from ML models so humans can explain and interpret the overarching AI system
The ML Life Cycle: Follow an ML model on its journey from conception to production
MPM in the ML Life Cycle: Learn how MPM can provide full visibility into issues that arise when training, deploying, and monitoring models
MPM and Responsible AI: Explore ways to ensure that your AI systems are built with responsibility in mind



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