How Domino accelerates AI/ML validation at scale
October 15th at 10 am PT | 1 pm ET
Register now
Model validation doesn’t have to be manual, fragmented, or reactive. In fact, it can be a competitive advantage—when it's automated, collaborative, and built into the model lifecycle from day one.
In Part 2 of our series, we’ll show you how Domino helps financial services and insurance organizations modernize their model validation workflows from environment provisioning to automated audit trails. You’ll see how our unified platform enables validators, builders, and IT to work in parallel, not in silos cutting validation timelines by 50–70%.
We’ll walk through:
- What validation looks like inside the Domino platform
- How to eliminate weeks of rework with one-click environment replication
- How automated documentation and real-time collaboration reduce audit findings
- Real examples of customers who’ve slashed validation timelines while enhancing compliance
Whether you're a model validator, IT leader, or data science exec, you’ll leave with a blueprint for bringing safe, scalable AI to production.
Featured Speakers
Mike Upchurch
VP, Financial Services and Insurance Strategy
Mike Upchurch is the Vice President of Strategy for Financial Services at Domino Data Lab, bringing over 25 years of expertise in analytics, ML/AI, business strategy, and technology. Previously, Mike held roles at Capital One as a product manager in their innovation lab and as a strategy and operations consultant in their Center for Machine Learning. Mike led strategy at Notch and in the mortgage lending group of Bank of America and was the co-founder of Fuzzy Logix. Prior to that he developed deep hands-on technical experience at The Hunter Group and PwC.
Nick Goble
Director of solution architecture for FSI
Nick Goble, Ph.D. leads Solution Architecture for Financial Services & Insurance at Domino Data Lab, bringing more than ten years of experience across quantitative finance, derivatives modeling, and fintech innovation. At Venerable, Nicholas managed Quantitative Research and Development, where he established quant research capabilities from the ground up and guided teams in building sophisticated trading platforms and pricing engines. Before that, he was a Senior Quantitative Researcher at Chatham Financial, focusing on valuation methodologies and bringing machine learning models into live trading environments. Nicholas holds a Ph.D. in Physics from Case Western Reserve University.
Ian McKenna
Solutions engineer for FSI
Ian McKenna is a Staff Sales Engineer at Domino Data Lab, focusing in Financial Services with over 15 years of experience in quantitative finance, risk management, and econometrics. Prior to joining Domino, Ian served as the Director of Field Engineering at dotData, where he led the field engineering team and helped drive the growth in the subprime lending space. Earlier in his career, he was a Principal Application Engineer at MathWorks, where he supported financial services clients with his advanced knowledge in AI/ML, distributed computing, optimization, and technical computing. Ian holds a Ph.D. from Northwestern University and a B.S. from the University of Florida in Materials Science.
Dylan Storey
Senior director, data science services
I am a scientifically minded leader with a background that includes microbial food safety research and a genome science PhD. I enable companies to build and maintain organizations and platforms for the capture and organization of data so that they may make better decisions based on evidence. My data work experience has been focused on empowering data-driven decision making and expanding data capabilities through training, technology, and process improvement. I have built high-performing teams that consistently deliver the highest throughput without burnout or churn, and I emphasize leadership by example and with empathy. I spent nearly a decade leveraging high performance computing to analyze and manage large, complex data sets in my academic research. My professional interests include process and decision automation through machine learning and common sense, software development, data engineering/architecture, and reproducible research. I earned my PhD from University of Tennessee and completed both my BS and MS at Cal State Fresno.