Engineering Your AI Platform // Panel // DE4AI 

//Abstract
To build a solid AI platform, it’s important to zero in on what really matters. This panel will dive into the key lessons from the evolution of data engineering and MLOps, including how the industry shifted from niche tools like feature stores to broader platforms. They'll discuss whether separate data and ML platforms are necessary or more effective when integrated, particularly for companies with smaller data teams. By taking a step back and looking at what’s actually worked in the world of MLOps and the recent buzz around LLMs, this panel will also dive into the merging roles of data engineering, analytics, MLOps, and whether the distinct ML engineer role is still relevant. Finally, they’ll share insights on designing an AI platform that’s practical, future-proof, and free from unnecessary complexity.
//Bio
Tobias Macey: Associate Director of Platform and DevOps Engineering @ Massachusetts Institute of Technology (MIT)
Daniel Svonava: CEO & Co-founder @ Superlinked
Colleen Tartow: Field CTO @ VAST Data
A big thank you to our Premium Sponsors ‪@Databricks‬, ‪@tecton8241‬, & ‪@onehouseHQ‬for their generous support!