Abstract: 

This session will explore and demonstrate how DataRobot's MLOps can speed up deployment, monitor drift and accuracy, ensure governance and ongoing model lifecycle management, including how to do automation retraining and have challenger models in production. Also, this session will cover how to deploy and monitor models built outside of DataRobot.

Bio: 

Asger holds a master of Computer Science and has for many years been leading software development teams as the CTO of several startup companies. Following that he has been helping many of the biggest enterprises around the world becoming successful with monitoring critical application services in production, and today at DataRobot he is focused on bringing some of the best practices from the DevOps world into the new growing field of MLOps, where some of the principles can be reused but where there is also a great need to have AI and Machine Learning specific capabilities to be able to scale in a standardised and governed way.