Monzo Machine Learning Case Study
This episode is a deep dive session on Monzo Bank's infra and how they are doing ML. We also touched on some of his greatest learnings over the years while trying to put ML into prod. One of the highlights came when we touched on how they go about assessing if a problem even needs ML in the first place. We also dove into what the structure of the Monzo ML teams look like and how they manage to efficiently collaborate. Consider this the Monzo case study you didn't know you needed.
In this episode
Neal Lathia
Senior Data Science Manager, Monzo
👋 Hi, I’m Neal - I’m currently the Machine Learning Lead at Monzo in London, where we’re focusing on building machine learning systems that optimise the app and help the company scale. ✈️ Before joining Monzo, I was a Data Scientist at Skyscanner, where I built recommender and ranking systems to improve travel information in the app. 🏫 Before Skyscanner, I was a Senior Research Associate in the Computer Lab at the University of Cambridge, working on healthcare mobile apps that use smartphone sensors. I spun out this research into a startup that was part of Accelerate Cambridge in the Judge Business School. 🎓 I did my MSci (in Computer Science), PhD (on recommmender systems), and first postdoctoral research position (on Urban Data Science) in the Department of Computer Science at University College London, where I am still an Honorary Research Associate. My PhD focussed on methods for evaluating collaborative filtering algorithms over time. While at UCL, I also spent time as a visiting researcher at Telefonica Research, Barcelona and worked as a Data Science consultant. My work has always focused on applications that use machine learning - this has taken me from recommender systems to urban computing and travel information systems, digital health monitoring, smartphone sensors, and banking. You can read more about my work and research in the Press & Speaking and Research sections.
Demetrios Brinkmann
Host
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.
David Aponte
Host
David is one of the organizers of the MLOps Community. He is an engineer, teacher, and lifelong student. He loves to build solutions to tough problems and share his learnings with others. He works out of NYC and loves to hike and box for fun. He enjoys meeting new people so feel free to reach out to him!