Abstract: 

The structure of how enterprises are delivering and consuming AI has changed drastically with the proliferation of open-source technology. The focus has shifted from tooling and platforms focused solely on model development to tools and platforms focused on the overall usage, consumption, and management of models. This emerging field is called Machine Learning Operations or MLOps. MLOps delivers ROI for those organizations that invested in "full-stack" ML technology, from development to operationalization, monitoring, and management.

Session Outline
The inherent challenges of deploying ML at scale and how to overcome them.
How to eliminate AI-related risks by adopting best practices for MLOps.
Measuring the quality of ML in production over-time with ML-focused monitoring.
What ML production lifecycle management is and why it matters.

Bio: 

Seph Mard joins DataRobot as a recognized industry leader of enterprise model risk management, model validation, model governance and best practices. Seph has more than a decade of experience applying data science to quantitative finance and risk management. As Director of Technical Product, Seph is a leader on DataRobot Product Management team where he is focused exclusively on ML Ops product management and strategy. Seph is bringing innovation into the world of Machine Learning Operations using DataRobot’s superior machine learning automation and data science edge. He holds dual M.Sc. degrees in Applied Mathematics and Econometrics.