Date | 14-16 September 2026
Location | New York, USA
Harness practical ML development and revenue-driven deployment to unlock business value while ensuring strong governance, thorough monitoring, and high-quality data
Artificial Intelligence and Machine Learning have moved beyond experimentation and into production environments across industries. Organizations are under increasing pressure to translate AI investments into measurable business outcomes while ensuring models remain trustworthy, compliant, and operationally reliable. However, many ML initiatives still struggle to transition from technically promising prototypes to scalable, production-ready systems that deliver real business value. This conference brings together AI and ML leaders to address the most pressing challenges in developing, deploying, governing, and monetizing machine learning models. Through practical insights and industry case studies, delegates will explore how to build robust AI systems that perform reliably in real-world environments while maintaining regulatory and organizational trust.
The GFMI 4th Annual Development, Implementation and Management of AI and ML Models conference will explore how AI and ML are moving from experimentation to enterprise impact, covering practical model development and implementation, strategies to generate measurable revenue, and the evolving opportunities and risks of large language models. From strengthening governance and validation frameworks to ensuring the right balance of data quantity and quality, the program focuses on turning technical capability into sustainable business value. Through real world case studies, expert led panels, and interactive discussions, attendees will gain actionable insights into building production ready AI and ML solutions, scaling them responsibly, managing model risk, and embedding strong oversight across the lifecycle.
Topics Covered:
- Understand when models overthink to realize the value and avoid the pitfalls of large reasoning models in finance
- Strengthen trust and adoption to prevent technically strong ML models from failing
- Leverage governance frameworks to support innovation while maintaining compliance
- Leverage AI/ML models to unlock new revenue opportunities and drive measurable business growth
- Strengthen cross-department communication and training to uphold governance standards
- Maintain data quality across the model lifecycle to ensure reliable AI/ML outcomes
Best Practices and Case Studies from:
- Sanjay Rohira, Global Head of Data, Model & AI Governance, Vanguard
- Freddy Lecue, Managing Director, Head of Frontier AI Models and Methodology, Wells Fargo
- Patrick Buckley, Head, Quantitative Model Development, J. P. Morgan Chase
- Casandra Clements Kerr, Senior Vice President, Program Director, Model Risk Management, U.S. Bank
- Scott Eilerts, Model Governance Strategist, American AgCredit
- Sam Henkel PhD, Executive Director, Model Development, UBS
Special discounts available to AGRC members! For more information please contact: Stefanos Ioannou, Digital Media and PR Executive at stefanosi@marcusevanscy.com or visit: https://tinyurl.com/294cc3zj
