In this episode, I'm speaking with Roey Mechrez from BeyondMinds. Roey holds a Ph.D. in Electrical Engineering, with vast experience in computer vision and deep learning research. We discuss the challenges of gluing together infrastructure solutions for an end-to-end ML platform, as well as generating monitoring insights for non-technical stakeholders and combating catastrophic forgetting.
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Timestamps:Â
00:00 Podcast intro
01:00 Guest introÂ
01:49 What does BeyondMinds do?Â
06:24 Audience for an end-to-end ML platformÂ
12:14 Communicating with non-technical stakeholders/usersÂ
15:03 The future of "AI-powered tools", and human-machine collaborationÂ
20:04 On complex system orchestration, generating insights from monitoring, and catastrophic forgetting – Biggest challenges in production MLÂ
25:23 Why is catastrophic forgetting a hard problem and how do you deal with it?Â
30:02 "Secret" tips on how to get started with automating the retraining processÂ
33:30 Generating monitoring insights and observations in a user-friendly formatÂ
38:12 Making data labeling issues explainable (automatically)Â
45:07 Customizing complex systems per user – Orchestrating an ML platformÂ
52:58 API design in ML platform componentsÂ
55:45 Measuring success for researchers, ML engineers, and software developers – can ML work fit into the Agile workflow.Â
1:02:22 Is "time to production" a good metric? Gains in time to production in the real worldÂ
1:06:02 How do you divide the work between ML researchers and engineers?Â
1:08:39 Recommendations for the audience
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Relevant Links:Â
A16z blog about AI
Data Science work in an agile environment –  A talk by Dima Goldenberg
Hayot Kis (Hebrew Podcast) חיות כיס
Data Engineering Podcast
ACX Podcast
Social Links:
https://www.linkedin.com/company/beyondminds/
https://www.linkedin.com/company/dagshub/
https://twitter.com/roeyme
https://twitter.com/DeanPlbn
https://twitter.com/TheRealDAGsHub
