How To Detect Silent Failures In ML Models
The webinar will teach you how to detect silent ML model failure without accessing the target data. We will cover the most likely causes for ML failure, like data and concept drift. You’ll learn the tools (both statistical and algorithmic) used in detecting and dealing with these failures, their applications, and their limits.
By the end of the webinar you will be able to:
- Detect drop in performance of their ML models without using ground truth data
- Monitor their Machine Learning models
- Understand and detect data drift to resolve the issues found
Co-Founder at NannyML
We are looking for passionate people willing to cultivate and inspire the next generation of leaders in tech, business, and data science. If you are one of them get in touch with us!