🧠 Algorithmic challenges in bringing ML models into production with Roey Mechrez, CTO at BeyondMinds

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.

Join our Discord community: https://discord.gg/tEYvqxwhah

---

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:

Social Links: