Chang She on Data Infrastructure for AI

Chang She on Data Infrastructure for AI

As a pandas core contributor and early Parquet adopter who built AI data pipelines at streaming company Tubi TV, Chang She saw firsthand why the traditional data stack breaks down for AI workloads—and founded LanceDB to fix it. Chang joined Ben Lorica to explain why vector databases are too narrow a solution for modern AI data needs, and what a true multimodal data infrastructure actually looks like. Chang and Ben get into why the Lance file format is quickly becoming the open source standard for multimodal data, how the rise of agents is exploding data infrastructure demands, why open-weight models are the enterprise cost shift to watch in the next 12 months, and more. "Trillion is the new billion," Chang says, and the enterprises that set up their data infrastructure now for that scale will be the ones that succeed.

Aishwarya Naresh Reganti on Making AI Work in Production

As the founder and CEO of LevelUp Labs, Aishwarya Naresh Reganti helps organizations “really grapple with AI,” and through her teaching, she guides individuals who are doing the same. Aishwarya joined Ben to share her experience as a forward-deployed expert supporting companies that are putting AI into production. Listen in to learn the value all roles—from data folks and developers to SMEs like marketers—bring to the table when launching products; how AI flips the 80-20 rule on its head; the problem with evals (or at least, the term “evals”); enterprise versus consumer use cases; and when humans need to be part of the loop. “LLMs are super powerful,” Aishwarya explains. “So I think you need to really identify where to use that power versus where humans should be making decisions.” Watch now.

Sharon Zhou on Post-Training

Post-training gets your model to behave the way you want it to. As AMD VP of AI Sharon Zhou explains to Ben on this episode, the frontier labs are convinced, but the average developer is still figuring out how post-training works under the hood and why they should care. In their focused discussion, Sharon and Ben get into the process and trade-offs, techniques like supervised fine-tuning, reinforcement learning, in-context learning, and RAG, and why we still need post-training in the age of agents. (It’s how to get the agent to actually work.) Check it out.

Fabiana Clemente on Synthetic Data for AI and Agentic Systems

Synthetic data has been around for a long time, decades even. But as KPMG’s Fabiana Clemente points out, “That doesn’t mean there aren’t a lot of misconceptions.” Fabiana sat down with Ben to clarify some of the current applications of synthetic data and new directions the field is taking—working with offshore teams when privacy controls just don’t allow you to share actual datasets, improving fraud detection, building simulation models of the physical world, enabling multi-agent architectures. The takeaway? Whether your data’s synthetic or from the real world, success often comes down to the processes you’ve established to build data solutions. Watch now.

Aurimas Griciūnas on AI Teams and Reliable AI Systems

SwirlAI founder Aurimas Griciūnas helps tech professionals transition into AI roles and works with organizations to create AI strategy and develop AI systems. Aurimas joins Ben to discuss the changes he’s seen over the past couple years with the rise of generative AI and where we’re headed with agents. Aurimas and Ben dive into some of the differences between ML-focused workloads and those implemented by AI engineers—particularly around LLMOps and agentic workflows—and explore some of the concerns animating agent systems and multi-agent systems. Along the way, they share some advice for keeping your talent pipeline moving and your skills sharp. Here’s a tip: Don’t dismiss junior engineers.