06-15, 10:30–11:10 (Europe/London), Warwick
Controlled environments such as banks are characterized by stringent data governance, model risk policies and operational protocols. These present unique challenges for data science teams to deliver business and customer value. While these constraints manage model and technology risk, they often impede agility and experimentation - key drivers of innovation in data science.
This talk discusses how we've managed to scale model training and deployment by 10X with our existing on-prem data science platform.
In the realm of banking, where data governance and model risk policies are paramount, data science teams face a unique set of challenges that can stifle innovation and slow down progress.
This session delves into how our team has successfully navigated these hurdles, implementing innovative strategies that have led to tenfold increase in our model training and deployment capabilities, all within the confines of our on-prem data science platform. We'll share insights into the methodologies and technologies that have empowered us to not only meet but exceed operational protocols without compromising on model or technology risk.
Attendees will leave with a greater understanding of how to foster agility and experimentation in a controlled environment, driving significant business and customer value.
Previous knowledge expected
I am a Lead Data Scientist with over 8 years of experience. I have a passion for using data science to create innovative solutions, and led several high profile projects with significant business impact.
Head of Data Science at Barclays UK
Aaron has developed numerous successful machine learning based products and capabilities across banks in Europe and Asia. His academic background is in physics and simulation science.