Abstract:In this session, we will talk about why monitoring is critical to ML success. ML models can fail silently and lose their predictive power. This talk will
In this session, we will talk about why monitoring is critical to ML success. ML models can fail silently and lose their predictive power. This talk will discuss the key reasons models fail and hurt business performance: model drift, data integrity, outliers and bias. Once identified, operational issues are time consuming to fix. This talk will focus on how cutting-edge Explainable AI and model analytics can help find the root cause of an operational issue quickly. MLOps is iterative. This talk will also outline how model and cohort comparisons can help reduce time to market for new models.
What You’ll Learn:
– How to measure model drift and how it can help identify model degradation, even without ground truth
– How to monitor for key model metrics
– How to root cause issues with Explainable AI and model analytics
– How to build high performance ML iteratively with model and cohort comparisons
– How bias and outlier detection can help in model monitoring success
Amit is the co-founder and CPO of Fiddler, a Machine Learning Monitoring company that empowers companies to efficiently monitor and troubleshoot ML models with Explainable AI. Prior to founding Fiddler, Amit led the shopping apps product team at Samsung and founded Parable, the Creative Photo Network, now part of the Samsung family. He also led PayPal’s consumer in-store mobile payments launching innovations like hardware beacon payments and has developed successful startup products, particularly in online advertising – paid search, contextual, ad exchange, and display advertising. Amit has passions for actualizing new concepts, building great teams, and pushing the envelope. He aims to leverage these skills to help define how AI can be fair, ethical, and responsible.
(Thursday) 1:35 PM - 2:05 PM