Product Management in Machine Learning // Laszlo Sragner // MLOps Meetup #54

MLOps community meetup #54! Last Wednesday we talked to Laszlo Sragner, Founder, Hypergolic.

// Abstract:
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?

// Bio:
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. He currently runs Hypergolic (hypergolic.co.uk) an ML Consulting company helping startups and enterprises bring the maximum out of their data and ML operations.

// Takeaways
Continuous evaluation and monitoring is indistinguishable in a well set up 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.

// Other Links
Free eBook about MLPM: https://machinelearningproductmanual.com/
Lightweight MLOps Python package: https://hypergol.ml/
Blog: laszlo.substack.com

----------- Connect With Us ✌️-------------   
Join our Slack community:  https://go.mlops.community/slack
Follow us on Twitter:  @mlopscommunity
Sign up for the next meetup:  https://go.mlops.community/register  

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Laszlo on LinkedIn: https://www.linkedin.com/in/laszlosragner/

Timestamps:
[00:00] Introduction to Laszlo Sranger
[02:15] Laszlo's Background
[09:18] Being a Quant, then influenced, what you were doing with the Investment Banks?
[12:24] Do you think this can be applied in different use cases or specific to what you are doing?
[14:41] Do you have any thoughts of a potentially highly opinionated person?
[16:54] Product management in Machine Learning
[24:59] You have to be at a large company or you have to have a large team? [26:38] What are your thoughts on MLOps products helping with product management for ML? Is it an overreach or scope creep?
[32:00] In the messy world of startups due to the big cost of an MVP for NLP is RegEx which means to user feedbacks it's incorporated by tweaking RegEx?
[33:04] Does the ensemble recent models more than older models? If so, what is the decay rate of weights for older models?
[35:40] Since the iterative management model is generic enough for most ML projects, which component of it can be easily generalized and tools built for version control?
[36:38] Topic Extraction: What type of model do you train for that task?
[52:55] Thoughts on Notebooks
[53:34] "I don't hate notebooks. Let's be clear about that. I put it this way, notebooks are whiteboards. You don't want your whiteboards to be your output because it's a sketch of your solution. You want the purest solution."