Operationalizing Machine Learning at a Large Financial Institution

Name: Operationalizing Machine Learning at a Large Financial Institution
Uploaded: 2021-03-17

Posted Mar 17, 2021 | Views 289

# Case Study

# Interview

# FinTech

# Regions.com

SPEAKERS

Daniel Stahl

Head of Data and Analytic Platforms @ Regions

Daniel Stahl leads the ML platform team at Regions Bank, and is responsible for tooling, data engineering, and process development to make operationalizing models easy, safe, and compliant for Data Scientists.

Daniel has spent his career in financial services and has developed novel methods for computing tail risk in both credit risk and operational risk, resulting in peer-reviewed publications in the Journal of Credit Risk and the Journal of Operational Risk. Daniel has a Masters in Mathematical Finance from the University of North Carolina Charlotte.

Daniel lives in Birmingham, Alabama with his wife and two daughters.

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Daniel lives in Birmingham, Alabama with his wife and two daughters.

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Demetrios Brinkmann

Chief Happiness Engineer @ MLOps Community

At the moment Demetrios is immersing himself in Machine Learning by interviewing experts from around the world in the weekly MLOps.community meetups. Demetrios is constantly learning and engaging in new activities to get uncomfortable and learn from his mistakes. He tries to bring creativity into every aspect of his life, whether that be analyzing the best paths forward, overcoming obstacles, or building lego houses with his daughter.

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SUMMARY

The Data Science practice has evolved significantly at Regions, with a corresponding need to scale and operationalize machine learning models. Additionally, highly regulated industries such as finance require heightened focus on reproducibility, documentation, and model controls. In this session with Daniel Stahl, we will discuss how the Regions team designed and scaled their data science platform using devops and mlops practices. This has allowed Regions to meet the increased demand for machine learning while embedding controls throughout the model lifecycle. In the 2 years since the data science platform has been onboarded, 100% of data products have been successfully operationalized.

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