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London is a hub for the world’s most well-known enterprises and organisations. Rev brings together data science and IT leaders at the cutting edge of AI innovation in industries such as financial services, life sciences, and media and entertainment.
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.


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.
How does one of the world's largest marketing groups turn vast client data into competitive advantage? In this conversation with WPP's global head of data science, hear how WPP built AI apps before it was mainstream and is now deploying agentic AI to unlock real-time intelligence for global brands.
AI amplifies what's already there—good or bad. Hear honest lessons from building two contrasting systems: a deterministic pricing engine and an agentic financial planning tool. Discover where AI genuinely elevates, where it amplifies chaos, and why governance and observability make all the difference.
Leading life science companies have deeply embedded Domino across the organisation, from benchmarking foundation models in clinical development to automating QC workflows. Learn how Domino's compliant environment became the foundation for rapid, grassroots AI adoption at enterprise scale.
The real AI bottleneck isn't the model—it's infrastructure. Hear first-hand lessons from building an enterprise data science platform across decentralised teams in analytics, risk, and audit, and learn why organisations consistently underestimate the skills and investment required to scale.
Modernising a statistical computing environment is more than a technology upgrade. Discover how pharma companies are evolving legacy SCEs in regulated contexts—consolidating technical debt, containing scope, and building the right foundations across people, process, and IT.
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.
As AI reshapes data science, BNP Paribas Cardif's AI Engineering team is enabling app-based, self-service AI. Learn how they developed a production-ready prototype of their verbatim analyzer, designed to process feedback from all respondent types. Powered by an LLM-enhanced hybrid engine combining generative AI and statistical modeling, this solution demonstrates how deploying app on Domino accelerates value delivery across the business.
Getting AI to deliver in the life sciences sector requires more than good models. One pharma company shares how it's building the data infrastructure that makes AI usable in practice, including lessons learned from bridging the scientific and engineering sides of the work.
Many have been surprised by the so-called "recent" rise of AI. But those paying attention have seen this coming for many years - and it is tightly coupled with NVIDIA's pioneering of accelerated computing. Learn why this is the case, and what's most crucial to consider for your enterprise to thrive during this time of unrelentingly rapid change.
Moving from fragmented infrastructure to a unified AI-powered platform that serves the world's largest brands is no easy feat. WPP will share their journey to build a client intelligence platform - covering data centralization, change management, and the next frontier: custom propensity models trained on client data, and AI-driven media activation.
Eduardo Arino de la Rubia of Central European University, and formerly Sr. Director of Data Science at Meta, will share how data science organizations and data scientist skillsets must evolve for the agentic era, and discuss diagnostics for calibrated confidence - in a time of multidimensional uncertainty.

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