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.
- Learn how Domino Data Lab and AWS integrate to provide end-to-end orchestration of MLOps
- Increase knowledgebase of Domino Model Monitor, a product that automates the process of tracking dataset drift and any degradation of model quality over time
- Watch Domino Data Lab act as the system of record for Data Scientists, tracking all actions, activities and outputs generated by Sagemaker Autopilot models.
- Kirk Borne PhD - Top Influencer, Chief Science Officer at DataPrime, Inc
- Rumi Olsen - AI/ML Specialized Solutions Architect at Amazon Web Services
- David Bloch - Data Science Evangelist at Domino Data Lab