Abstract: 

As AI becomes an essential part of competitive business strategies, companies need efficient ways to integrate it into their products and workflwos. At The Fitch Group, the Emerging Technology team has developed a scalable approach to LLMOps and MLOps, creating a ""backbone"" of reusable AI components that multiple teams can readily integrate with support from our AI Implementation team. This approach significantly reduces the time and resources needed to deploy and manage AI at scale, making the technology accessible across the organization without major increases in headcount.

This session will provide senior leaders a business-focused overview of how scalable AI operations can be implemented within enterprise financial services. We’ll discuss our structured approach to manage intake of AI integration requests, prioritize them efficiently, and ensure effective governance for rapid deployment. By focusing on clear yet adaptable processes, our team enables quick implementation of reusable components across departments, making AI integration achievable without the need for deep technical expertise in every team.

Key takeaways from this talk will include insights on structuring teams for efficient AI deployment, strategies to achieve rapid AI scalability, and a discussion of which reusable Ops components empower non-specialist teams to incorporate AI into their work. The presentation will appeal to senior leaders who have a fundamental understanding of MLOps & LLMOps technical aspects and want to understand the business impact of different operational strategies for enterprise-level AI integration.

Bio: 

Pablo Vega-Behar is a Director of Machine Learning Engineering at Fitch Group, a financial services firm. With over a decade of experience in data science and AI, his expertise spans multiple industries including healthcare, consulting, civil engineering, and finance. He earned his Ph.D. in Engineering from Georgia Tech and is currently focused on applied generative AI, automating content generation in regulated industries, as well as machine learning applications for financial analysis and risk management.