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 time 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 organizationâ??s machine learning workflow. Youâ??ll 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. This report covers: <ul><li>MPM and Explainability: Explore a data-centric framework for producing high-quality ML and AI models and systems</li><li>Explainable AI (XAI): Generate explanations from ML models so humans can explain and interpret the overarching AI system</li><li>The ML Life Cycle: Follow an ML model on its journey from conception to production</li><li>MPM in the ML Life Cycle: Learn how MPM can provide full visibility into issues that arise when training, deploying, and monitoring models</li><li>MPM and Responsible AI: Explore ways to ensure that your AI systems are built with responsibility in mind</li></ul>

Model Performance Management with Explainable AI PDF

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