The presentation introduces Intuit AI Model Monitoring Service (MMS). MMS is an in-house Spark-based solution developed by Intuit AI to provide ongoing monitoring for both data (statistics of model input/output etc.) and model metrics (precision, recall, AUC etc.) of in-production ML models. The project is soon to be open-source. MMS aims to tackle multiple challenges of in-production ML model monitoring:
Dr. Qingbo Hu received his Ph.D degree in 2016 from University of Illinois at Chicago, where his research advisor was Philip S. Yu. He is currently a senior business analytics associate in LinkedIn's Analytics team. Dr. Hu has a broad interest in the research topics related to data mining/machine learning theories and techniques, as well as how to adopt them to solve real-life business problems. He has numerous research publications in many major data mining conferences, such as KDD, ICDM, SDM, WWW, CIKM and etc.
Sumanth is a Software Engineer at Intuit, where he focuses on building scalable ML services. He has previously built Big data and ML systems at Verizon and Apple. Sumanth holds a Masters in Computer Science from the University of North Carolina at Charlotte.