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: 

00:00 Podcast intro
01:00 Guest intro 
01:49 What does BeyondMinds do? 
06:24 Audience for an end-to-end ML platform 
12:14 Communicating with non-technical stakeholders/users 
15:03 The future of "AI-powered tools", and human-machine collaboration 
20:04 On complex system orchestration, generating insights from monitoring, and catastrophic forgetting – Biggest challenges in production ML 
25:23 Why is catastrophic forgetting a hard problem and how do you deal with it? 
30:02 "Secret" tips on how to get started with automating the retraining process 
33:30 Generating monitoring insights and observations in a user-friendly format 
38:12 Making data labeling issues explainable (automatically) 
45:07 Customizing complex systems per user – Orchestrating an ML platform 
52:58 API design in ML platform components 
55:45 Measuring success for researchers, ML engineers, and software developers – can ML work fit into the Agile workflow. 
1:02:22 Is "time to production" a good metric? Gains in time to production in the real world 
1:06:02 How do you divide the work between ML researchers and engineers? 
1:08:39 Recommendations for the audience

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Relevant Links: 

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