Javier de la Rúa Martínez
Javier is a Research Engineer at Hopsworks, where he actively contributes to advancing the Hopsworks AI Lakehouse. He is currently pursuing his Ph.D. at KTH Royal Institute of Technology in Sweden with a primary focus on large-scale machine learning systems.
Session
Operationalizing ML isn’t just about models — it’s about moving and engineering data. At Hopsworks, we built a composable AI pipeline builder (Brewer) based on two principles: Tasks and Data Sources. This lets users define workflows that automatically analyse, clean, create and update feature groups, without glue code or brittle scheduling logic.
In this talk, we’ll show how Brewer drives the automation of feature engineering, enabling reproducible, declarative pipelines that respond to changes in upstream data. We’ll explore how this fits into broader ML workflows, from ingestion to feature materialization, and how it integrates with warehouses, streams, and file-based systems.