Even as AI provides significant benefits - like creating new revenue opportunities, increasing productivity, decreasing costs, and fostering innovation in outdated business models - there is a big potential for unintentional errors from outcomes that are unclear, biased and non-compliant.
Companies often realize AI and ML performance issues after the damage has been done, which in some cases has made headlines.
Join us on this webinar to learn why monitoring is critical to successful AI:
- Get visibility and insights into the AI Blackbox to understand the why and how behind AI models
- Understand the 5 key operational challenges in AI and ML models - model decay, data drift, data integrity, outliers, and bias - and how to overcome them
- Learn how Explainable ML Monitoring is the ideal way to overcome these challenges
- Build transparent AI models that are continuously monitored and are less risky
Join Fiddler's Founder & CPO, Amit Paka, and Data Scientist, Aalok Shanbhag, as they discuss how Fiddler’s Explainable Monitoring engine overcomes the five key operational challenges in AI and ML models and how to achieve risk-free and reliable AI outcomes.