What to do (and what NOT to do) when deploying ML models
Online session presented by Monitaur CTO Dr. Andrew Clark and Research Scientist Sid Mangalik
Machine Learning model deployment can be a complicated undertaking, but it doesn’t have to be overly complex. During this 60-minute event, presented by Dr. Andrew Clark and Sid Mangalik, we will cover:
This presentation is built for data scientists who want to strengthen their foundational understanding of model deployment as well as data science leaders who want to create paradigms for their teams to follow. It's also geared toward anyone passionate about Data Science, Python, and Responsible AI.
Enjoy the replay of this best practices session!
Dr. Andrew Clark is Monitaur’s co-founder and Chief Technology Officer. A trusted domain expert on the topic of ML auditing and assurance, Andrew built and deployed ML auditing solutions at Capital One. He has contributed to ML auditing education and standards at organizations including ISACA and ICO in the UK. He currently serves as a key contributor to ISO AI Standards and the NIST AI Risk Management framework. Prior to Monitaur, he also served as an economist and modeling advisor for several very prominent crypto-economic projects while at Block Science.
Andrew received a B.S. in Business Administration with a concentration in Accounting, Summa Cum Laude, from the University of Tennessee at Chattanooga, an M.S. in Data Science from Southern Methodist University, and a Ph.D. in Economics from the University of Reading. He also holds the Certified Analytics Professional and American Statistical Association Graduate Statistician certifications. Andrew is a professionally trained concert trumpeter and Team USA triathlete.
Sid Mangalik is a Ph.D. candidate in natural language processing at Stony Brook University working with Andy Schwartz. His previous studies focused on the intersection of artificial intelligence and psychology, where he worked with Ritwik Banerjee on language use in scientific writing and among abusers. Sid's previous experience also includes a role at Capital One as a Data Engineer.
His current research is working on identifying connections between community-level language and psychological outcomes. He is a Research Scientist at Monitaur, a start-up focused on making machine learning models fairer, safer, and more transparent.