Businesses have to fundamentally shift how they manage the data science lifecycle as machine learning models become integral to business processes. In particular, enterprise-grade model monitoring and management capabilities are proving critical for responding to rapidly changing events and data. You can’t depend on data science if you don’t know it’s performing correctly.
Join this session to learn about real-world, industry-specific scenarios with data science experts from AWS and Domino Data Lab as they discuss the importance of a “single pane of glass” that records all activities, results, assumptions, and outputs relating to enterprise model development and operationalization in order to manage model and data drift.
We’ll also showcase a practical example of how Domino Data Lab integrates with Amazon SageMaker with a walk-through of running Autopilot (AutoML) inside Domino. Learn how these technologies combine training capabilities from SageMaker, monitoring capabilities from Domino Model Monitor, and centralization of data science work in the Domino Data Lab platform.