In this talk, we’ll showcase, through ML monitoring and notebooks, how data scientists and ML engineers can leverage ML monitoring to find the best data and retraining strategy mix to resolve machine learning performance issues. This data-driven, production-first approach enables more thoughtful retraining selections, shorter and leaner retraining cycles, and can be integrated into MLOps CI/CD pipelines for continuous model retraining upon anomaly detection.

This session will cover architectural considerations for multi-tenancy in ML, best practices in traditional software engineering that can be copy/pasted over to MLOps, as well as new considerations unique to ML