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Tuesday May 26, 2026 10:00 - 10:45 CEST
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AI is moving fast - and so are the decisions that come with it. For engineering teams working in complex, real-world systems, the challenge isn’t access to AI technology. It’s knowing where to start, what to build, and how to make the right choices across an increasingly sophisticated stack.
This talk shares practical approaches to identifying high-value AI use cases, drawn from experience designing and deploying production machine learning systems inside one of the world’s most demanding environments - global financial market surveillance across more than 50 exchanges and 15 regulators worldwide.
A core theme is the relationship between problem definition and technology selection. Before choosing a tool, understanding the process, the inputs and outputs, and what genuinely better looks like tends to produce stronger outcomes - and clearer architecture decisions.
The session also explores how classical ML, generative AI, and agentic systems can work together effectively. Rather than viewing these as competing approaches, the talk offers a framework for thinking about each as a distinct capability - and for combining them deliberately to build systems that are greater than the sum of their parts.
Attendees will leave with practical frameworks they can apply to their own environments, a clearer mental model for navigating the modern AI stack, and fresh perspectives on how some of the most complex AI systems in the world are being designed and deployed today.
Speakers
avatar for Sarah Bradley

Sarah Bradley

Director of Data Science & AI, NASDAQ
Sarah Bradley is Director of Data Science & AI at Nasdaq, where she leads the development and deployment of machine learning, generative AI, and agentic AI capabilities across Nasdaq Market Surveillance - a platform used by more than 65 exchanges and regulators globally to detect... Read More →
Tuesday May 26, 2026 10:00 - 10:45 CEST
1 - MAIN STAGE

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