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Monzo's Machine Learning Stack

Posted Apr 23, 2023 | Views 340
# Machine Learning Stack
# Monzo
# ML Frameworks
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SPEAKER
Neal Lathia
Neal Lathia
Neal Lathia
Staff Machine Learning Engineer @ Monzo

Neal is currently a Staff Machine Learning Engineer at Monzo Bank in London, where he focus on building machine learning systems to help make money work for everyone (reviewed in 2020, 2021, 2022) and Monzo’s machine learning platform. :airplane: Before joining Monzo, Neal was a Senior Data Scientist at Skyscanner, where he built recommender and ranking systems to improve travel information in the app.

:school: / :calling: Before Skyscanner, Neal was a Senior Research Associate in the Computer Lab at the University of Cambridge, working on healthcare mobile apps that use smartphone sensors. He spun out this research into a startup that was part of Accelerate Cambridge in the Judge Business School.

:mortar_board: Neal did an 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 he's still an Honorary Research Associate. While at UCL, Neal also spent time as a visiting researcher at Telefonica Research, Barcelona and worked as a Data Science consultant.

:bulb: Neal's work has always focused on systems that use machine learning - this has taken me from recommender systems to urban computing and travel information systems, digital health monitoring, smartphone sensors, banking, and open source machine learning tools. You can read more about Neal's work and research in the Press & Speaking

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Neal is currently a Staff Machine Learning Engineer at Monzo Bank in London, where he focus on building machine learning systems to help make money work for everyone (reviewed in 2020, 2021, 2022) and Monzo’s machine learning platform. :airplane: Before joining Monzo, Neal was a Senior Data Scientist at Skyscanner, where he built recommender and ranking systems to improve travel information in the app.

:school: / :calling: Before Skyscanner, Neal was a Senior Research Associate in the Computer Lab at the University of Cambridge, working on healthcare mobile apps that use smartphone sensors. He spun out this research into a startup that was part of Accelerate Cambridge in the Judge Business School.

:mortar_board: Neal did an 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 he's still an Honorary Research Associate. While at UCL, Neal also spent time as a visiting researcher at Telefonica Research, Barcelona and worked as a Data Science consultant.

:bulb: Neal's work has always focused on systems that use machine learning - this has taken me from recommender systems to urban computing and travel information systems, digital health monitoring, smartphone sensors, banking, and open source machine learning tools. You can read more about Neal's work and research in the Press & Speaking

+ Read More
SUMMARY

Neal Lathia, a staff ML engineer at Monzo, provides an overview of Monzo's journey from a prepaid card to a fully-fledged bank with over 7 million customers. Lathia shares his experience of building Monzo's ML platform and team and highlights some of the challenges they faced. He explains how Monzo's ML stack is built on top of their existing engineering and analytics foundations, and how they developed an ML Ops path to enable the deployment of ML models in a flexible and reliable way. Lathia also discusses the ML frameworks used by Monzo and the importance of speed and determinism in ML when dealing with transaction data.

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