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
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