Monzo Bank - An MLOps Case Study // Neal Lathia // MLOps Coffee Sessions #20

Coffee Sessions #20 with Neal Lathia of Monzo Bank, talking about Monzo Bank - An MLOps Case Study

//Bio
Neal is currently the Machine Learning Lead at Monzo in London, where his team focuses on building machine learning systems that optimise the app and help the company scale. Neal's work has always focused on applications that use machine learning - this has taken him from recommender systems to urban computing and travel information systems, digital health monitoring, smartphone sensors, and banking.

//Talk Takeaways
Monzo Bank has a small, but a very impactful team continuously learning new things. Optimistically do their utmost to avoid “throwing problems over the wall,” and so they build systems, iterate on machine learning models, and collaborate very closely with each other and with many folks across the business.

Hopefully, all of that paints a picture of a team that aims to bring real and valuable machine learning systems to life. Monzo does not spend time trying to advance the state-of-the-art in machine learning or tweak models to absolute perfection.

//Other links you can check Neal on
Personal Website: http://nlathia.github.io/
Research: http://nlathia.github.io/research/
Press & Speaking: http://nlathia.github.io/public/
http://nlathia.github.io/2020/06/Customer-service-machine-learning.html
http://nlathia.github.io/2020/10/ML-and-rule-engines.html
http://nlathia.github.io/2020/10/Monzo-ML.html http://nlathia.github.io/2019/09/Large-NLP-in-prod.html http://nlathia.github.io/2020/07/Shadow-mode-deployments.html  https://github.com/operatorai

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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with David on LinkedIn: https://www.linkedin.com/in/aponteanalytics/
Connect with Neal on LinkedIn: https://www.linkedin.com/in/nlathia/

Timestamps:
[00:00] Intro to Neal Lathia  
[02:48] Background of Monzo Bank
[05:06] Problems you're solving with Machine Learning at Monzo?  
[08:36] Why do you think it's fairly easy to frame a lot of problems using Machine Learning?  
[11:56] How do you decide on rule-based or Machine learning?  
[15:33] Team Structure  
[19:18] What are some challenges like size, latency and the like?
[21:52] How have you addressed learning skills/challenges in your team?  
[26:17] Do you have something that connects your team with all the metadata you have?
[27:14] Are you also having the monitoring models in your dashboard or is that something else?
[28:51] Why should I bring another tool that the company is not familiar with when we already have one?  
[31:43] Do you feel like there will be a point in time where you need to buy a tool because one problem is taking so much of your time?
[38:30] Engineering optimization teams for machine learning?  
[40:34] Take us through the idea to production?
[46:29] How do you deal with reproducibility?
[49:48] Do you have ethics people on the team?
[54:12] Why are you using GCP and AWS?
[56:09] What are these different used cases and how do they differ?
[57:57] How do you address applications that don't work?

Episode source