AI Monitoring and Explainability - the Critical, Hidden Connection
Model monitoring has become a critical capability that data scientists need to master in order to keep their models delivering throughout the ups and downs and sudden shocks of real world use. This session will cover key topics that data scientists should consider as they embark on a monitoring program for their company, including: Why is monitoring important? What should data science and MLOps teams be watching out for? We’ll cover the scope of key metrics that are important to managing a model’s ongoing success, as well as how to make sure that you’re setting your alert thresholds in a way that means you’re not going off on snipe hunts. How does ML explainability have anything to do with monitoring? We’ll show how explainability is key to root cause analysis and rapid debugging, so that your model stays in production longer.