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
Wojtek Kuberski-Data Science Dojo

Wojtek Kuberski

Co-Founder at NannyML

Wojtek Kuberski is a co-founder of NannyML, a startup for monitoring ML models in production. He holds a Master’s Degree in AI. He previously founded and grew an AI consultancy. He likes tennis, chess, and food. Wojtek’s Vita: I’ve always enjoyed automating things, so after stumbling upon AI during my bachelor’s, I did master’s in AI. One thing lead to another and I ended up founding Prophecy Labs – an AI consultancy. There I focused on building AI systems with huge real-world impact. Measuring the performance of AI systems in production was the biggest challenge for our clients. And that’s why I founded NannyML – a startup that monitors AI, ensuring that it continues adding business value

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!

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