Detecting and Handling Drift

In this webinar

The machine learning lifecycle extends beyond the deployment stage. Monitoring deployed models is crucial for continued provision of high quality machine learning enabled services. Key areas include model performance and data monitoring, detecting outliers and data drift using statistical techniques. Join our latest webinar with Arnaud van Looveren, Head of Data Science Research at Seldon, and Ed Shee, as they explore how to detect model drift, what methodologies exist for detecting drift, common mistakes make by organisations, and how to automate MLOps processes at scale to handle the issue.


Ed Shee

Head of Developer Relations, Seldon

Arnaud Van Looveren

Head of Data Science Research, Seldon

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