Even as artificial intelligence (AI) and machine learning (ML) provide endless benefits, such as increasing productivity while decreasing costs, reducing waste, improving efficiency, and fostering innovation in outdated business models, there’s tremendous potential for errors that result in unintended, biased results. Companies often realize AI and ML performance issues after the damage has been done.

Join us for this live webinar featuring NVIDIA Inception member Fiddler.ai to learn more about the basics of Explainable AI (XAI) and why explainable monitoring is critical to successful AI.

• Five key operational challenges in AI and ML models, including model decay, data drift, data integrity, outliers, and bias

• The ways Explainable ML Monitoring overcomes these challenges

• How to solve for bias and fairness issues with transparency and insights through continuous monitoring

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