Detecting and Handling Drift in Production

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.
In this webinar, we 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. 
Watch the webinar below.


Ed Shee
Ed Shee Head of Developer Relations, Seldon
Arnaud Van Looveren Head of Data Science Research, Seldon

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