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.
In this episode
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.
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.