Meetup #54

Product Management in Machine Learning

How my experience in quant finance and software engineering influenced how we ran ML at a London Fintech Startup. How to solve business problems with incremental ML? What's the difference between academic and industrial ML?


Continuous evaluation and monitoring is indistinguishable in a well setup product team. Separation of concerns (SE, ML, DevOps, MLOps) is very important for smooth operation, low friction team coordination/communication is key. To be able to iterate business features into models you need a modelling framework that can express these which is usually a DL package. DS-es are well motivated to go more technical because they see the rewards of it. All well run (from DS perspective) startups in my experience do the same.

In this episode

Laszlo Sragner

Laszlo Sragner

Founder, Hypergolic

Laszlo worked as a quant researcher at multiple investment managers and as a DS at the world's largest mobile gaming company. As Head of Data Science at Arkera, he drove the company's data strategy delivering solutions to Tier 1 investment banks and hedge funds. Laszlo currently runs Hypergolic ( an ML Consulting company helping startups and enterprises bring the maximum out of their data and ML operations.



Demetrios Brinkmann

Demetrios Brinkmann


Demetrios is one of the main organizers of the MLOps community and currently resides in a small town outside Frankfurt, Germany. He is an avid traveller who taught English as a second language to see the world and learn about new cultures. Demetrios fell into the Machine Learning Operations world, and since, has interviewed the leading names around MLOps, Data Science, and ML. Since diving into the nitty-gritty of Machine Learning Operations he felt a strong calling to explore the ethical issues surrounding ML. When he is not conducting interviews you can find him making stone stacking with his daughter in the woods or playing the ukulele by the campfire.