Machine Learning Models: Detecting Silent Failures

Learn about machine learning models and how to detect silent features in these models. This session will teach you how to detect silent machine-learning model failure without accessing the target data. We will cover the most likely causes of the Machine Learning model's 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 Table of Contents: 00:00 Introduction 05:33 Data drift and concept drift 10:17 Performance estimation 23:29 Data and concept drift detection 31:27 Summary 39:05 Demo 53:30 QnA -- For more captivating community talks featuring renowned speakers, check out this playlist: https://youtube.com/playlist?list=PL8eNk_zTBST-EBv2LDSW9Wx_V4Gy5OPFT To gain a better understanding of what data scientists do and how they work, check out this playlist: https://youtube.com/playlist?list=PL8eNk_zTBST9zccqrEhDDkjMZK1k3Aagl -- At Data Science Dojo, we believe data science is for everyone. Our data science trainings have been attended by more than 10,000 employees from over 2,500 companies globally, including many leaders in tech like Microsoft, Google, and Facebook. For more information please visit: https://hubs.la/Q01Z-13k0 💼 Learn to build LLM-powered apps in just 40 hours with our Large Language Models bootcamp: https://hubs.la/Q01ZZGL-0 💼 Get started in the world of data with our top-rated data science bootcamp: https://hubs.la/Q01ZZDpt0 💼 Master Python for data science, analytics, machine learning, and data engineering: https://hubs.la/Q01ZZD-s0 💼 Explore, analyze, and visualize your data with Power BI desktop: https://hubs.la/Q01ZZF8B0 -- Unleash your data science potential for FREE!