About 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.
Speakers
Ed Shee
Head of Developer Relations, Seldon
Arnaud Van Looveren
Head of Data Science Research, Seldon
What you'll learn
- How to detect model drift
- Methods for detecting drift
- Common pitfalls
- How to automate MLOps for drift
Watch the video
https://seldon.wistia.com/medias/0ooey6o6v8?embedType=async&seo=false&videoFoam=true&videoWidth=640