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Industrial Machine Learning and Building Tools for Data and Model Monitoring with Evidently AI Co-Founders Elena Samuylova and Emeli Dral

Podcast |: Machine Learning Engineered
Publisher |: Charlie You
Media Type |: audio
Podknife tags |: Artificial Intelligence,; Business,; Careers,; Data Science,; Interview,; Machine Learning,; Technology
Categories Via RSS |: Business,; Careers,; Science,; Technology
Publication Date |: Feb 16, 2021
Episode Duration |: 01:21:16

Description
iTunes Summary

Elena Samuylova and Emeli Dral are the co-founders of Evidently AI, where they build open source tools to analyze and monitor machine learning models. Elena was previously the head of the startup ecosystem at Yandex, director of business development at their data factory and chief product officer at Mechanica AI. Emeli was previously a data scientist at Yandex, chief data scientist at the data factory and Mechanica AI in addition to teaching machine learning both online and at multiple universities.

Learn more about Elena, Emeli, and Evidently AI:

https://evidentlyai.com/

https://twitter.com/elenasamuylova

https://twitter.com/EmeliDral

Every Thursday I send out the most useful things I’ve learned, curated specifically for the busy machine learning engineer. Sign up here: http://cyou.ai/newsletter

Follow Charlie on Twitter: https://twitter.com/CharlieYouAI

Subscribe to ML Engineered: https://mlengineered.com/listen

Comments? Questions? Submit them here: http://bit.ly/mle-survey

Take the Giving What We Can Pledge: https://www.givingwhatwecan.org/

Timestamps:

02:15 How Emeli and Elena each got started in data science

07:10 Applying machine learning across a wide variety of industries at the Yandex Data Factory

14:55 Using ML for industrial process improvement

23:35 Challenges encountered in industrial ML and technical solutions

27:15 The huge opportunity for ML in manufacturing

34:35 How to ensure safety when using models in physical systems

37:40 Why they started working on tools for data and ML monitoring

42:50 Different kinds of data drift and how to address them

48:25 Common mistakes ML teams make in monitoring

55:25 Features of Evidently AI's library

57:35 Building open source software

01:02:25 Technical roadmap for Evidently

01:05:50 Monitoring complex data

01:08:50 Business roadmap for Evidently

01:11:35 Rapid fire questions

Links:

Evidently on Github

Evidently AI's Blog

Thinking Fast and Slow

Flow

Doing Good Better

Elena and Emeli of Evidently AI discuss what they've learned applying ML across a wide variety of industries, including manufacturing and industrial process improvement, and then go into why they've started building tools for data and ML monitoring as well as how teams can do it better.