Romain Clement

MLOps was supposed to be the extension of DevOps applied to ML-centric software. But the story is not so simple, and in 2026 the collaboration between data scientists and engineers is still not so smooth in most organizations.

Deploying ML models doesn’t have to mean spinning up servers and writing backend code. This talk shows how to run machine learning inference directly in the browser—using ONNX and WebAssembly—to go from prototype to interactive demo in minutes, not weeks.

Have you ever spun up a Spark cluster just to update three rows in a Delta table? In this talk, we’ll explore how modern Python libraries can power lightweight, production-grade Data Lakehouse workflows—helping you avoid over-engineering your data stack.