Building AI & ML Platforms – Lessons from Min Cai’s Journey at Uber
Join us for the next Data Dialogs session, featuring Min Cai, who has spent nearly a decade as the custodian of Uber’s machine learning platform, one of the most advanced and sophisticated of its kind. This session is designed to help you explore strategic approaches to building AI and ML platforms, with a focus on the build vs. buy decision-making process, integrating generative AI, and measuring the value of these platforms.
The effectiveness of your machine learning, AI, and data science efforts depends largely on the flexibility and strength of the platform you build. As these fields evolve, having a solid infrastructure becomes essential for delivering meaningful results and adapting to new challenges. This session with Min Cai will offer key insights into platform development, decision-making strategies, and the role of generative AI, all based on real-world experience at scale.
Topics:
Building Scalable AI & ML Platforms
Min will share experiences and lessons learned in scaling platforms for machine learning and AI, with insights into the strategies, technologies, and infrastructure decisions that can help you develop and evolve your own systems.
Infrastructure and Build vs. Buy Strategies
Explore how to decide when to build in-house versus when to leverage third-party solutions, and how to evaluate which components will add the most value to your teams. Min will share key takeaways on this topic, drawn from his leadership of one of the world’s largest machine learning platforms.
Integrating Generative AI into Your Platform
As generative AI transforms industries, Min will discuss practical ways to integrate it into your existing systems, blending open-source models, commercial offerings, and custom solutions. He’ll explore how these integrations can drive innovation and create new opportunities for businesses.
Measuring Platform Value
Understand how to measure the impact and success of your AI and ML platforms, from tracking business value to evaluating long-tail use cases and adoption metrics. Min will share insights on how to assess both immediate and long-term returns on your AI investments.
Who Should Attend
Data Science Directors | Principal Data Scientists | ML Engineering Leaders | AI Strategy Decision Makers | Tech Executives
What to Expect
A practical discussion on how to approach building scalable AI & ML platforms.
Actionable insights into making strategic build vs. buy decisions for your AI infrastructure.
Hands-on advice for integrating generative AI and maximizing its value in established systems.
Interactive Q&A sessions designed to provide specific takeaways for attendees’ own initiatives.
Attendance
To ensure open and candid discussion, we are operating under Chatham House Rules, and recording is not permitted. Approved participants will receive confirmation with event details.
Featured Speaker
Min Cai
Min has spent the past decade developing and scaling AI and ML platforms, leading strategic decisions on how to build cutting-edge infrastructure. His experiences provide valuable insights into managing the complexities of AI systems at scale, with takeaways that can be applied across organizations of all sizes.
Your Hosts
Duncan Gilchrist
Duncan is an entrepreneur and data scientist passionate about making data and AI work for businesses and for the world. He was previously Director of Data Science for Uber Eats Marketplace and VP Data Science and Engineering at Gopuff.
Hugo Bowne-Anderson
Hugo is a data science and AI consultant who loves community building. He led Developer Relations at Outerbounds and Coiled, hosts the industry podcast Vanishing Gradients, and developed over 30 courses on the DataCamp platform that have reached more than 3 million learners worldwide.