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New York is home to the largest, most well-known enterprises in the world. Rev NYC brings together data science and IT leaders at the cutting edge of AI innovation in industries such as financial services, media and entertainment, technology, and the public sector.
Join us for direct conversations, success stories, and lessons learned on moving from AI experiments to production-grade systems with a lens on regulated industries.
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The guardrails built for predictive models were already straining under GenAI—now agentic systems are breaking them entirely. Discover the AI risk blind spots financial institutions can't afford to ignore, and how to move beyond principles toward eliminating real ethical, reputational, and regulatory exposure.
Enterprises have the models, the talent, and the budgets. What's missing is the operational foundation to turn AI investment into measurable business impact. Discover how a full enterprise AI application platform empowers teams to build, govern, and scale AI-powered decisioning systems.
Without standard modeling practices, technical debt accumulates fast. Learn how Fannie Mae built a structured, reproducible workflow orchestrator that brings trust, transparency, and governance to every stage of the model development process.
The long-awaited MRM guidance is here. But gen AI and agentic AI are explicitly out of scope. What does it mean when you’re scrambling to govern a rapidly-evolving AI landscape? Hear directly from senior MRM leaders at TIAA, Capital One, and an advisor who helped shape the original SR 11-7, on how the new guidance changes their approach, and what enterprise risk leaders should do now.
See how risk teams can run production ML and GenAI directly from Excel in a regulated bank scenario, with three interconnected Domino services: an expected loss pipeline, an AI safety guardrails layer, and traced agents for analysis and reporting. A code generator reads model signatures and auto-builds strongly typed Excel UDFs with full observability across every hop, no manual coding required.
See how agentic workflows on Domino can automate the most painful steps in model risk management: policy enforcement, documentation generation, and adversarial validation. All triggered as a single traced pipeline. From model development through approval, agents generate reports from testing results and metadata already in Domino, run validation probes, and update the governance bundle, turning weeks of manual back-and-forth into a reproducible workflow.
Get an inside look at data science at the Federal Reserve Bank of NY and hear how LLMs can be used to measure proxies relevant to research, such as sentiment Federal Reserve transparency. Mark Vandergon covers key research findings from a recent paper including lessons on consistency, rigor, reducing subjectivity, and bridging data science with economic frameworks through cross-functional collaboration.
How does a small data science team drive outsized impact in a global media company? At Vevo, we use AI and automation to scale our influence across the business, up-leveling our company capabilities while navigating governance and adoption challenges.
Vibe coding lowered the barrier; agentic engineering raises the stakes. As AI takes on full dev lifecycles, the challenge isn't autonomy—it's knowing when humans must stay in the loop. Learn to design HITL checkpoints, build governance, and scale toward dynamic virtual engineering teams.
Regulated enterprises don't have a model problem—they have an application problem. See live examples of AI applications driving real business outcomes, not just proof-of-concepts, and learn how enterprises can build and deploy production-ready AI apps in hours, not weeks.
The Federal Reserve's David Palmer, architect of SR 11-7, will share how banks are deploying AI, key supervisory considerations, and what regulators look for in governance and guardrails. Along with Domino COO Thomas Robinson, he'll unpack the new SR 26-2, discuss what genAI means (and doesn't) for model risk, validation, and the broader model development lifecycle.
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