Alejandro Saucedo
As the number of production machine learning use-cases increase, we find ourselves facing new and bigger challenges where more is at stake. Because of this, it's critical to identify the key areas to focus our efforts, so we can ensure we're able to transition from machine learning models to reliable production machine learning systems that are robsut and scalable. In this talk Alejandro dives into the key concepts, trends and lessons learned in production machine learning systems as of 2025. This talk will cover some of the nuances that make production machine learning so challenging, dive into some of the latest production ML tooling ecosystem, and reflect on some best practices and lessons learned that have been abstracted from production use-cases of machine learning operations at scale.
Alejandro is the Director of Engineering, Science & Product at Zalando SE, where he is responsible for some of the core large-scale AI & Data platforms that power Supply and Demand across the group. Alejandro is appointed as AI Expert at the United Nations and the European Commission, serves as Board Member at ACM's Board of Directors, and is the Scientific Adviser at the Institute for Ethical AI, where he has led contributions to EU policy, including the AI Act, the Data Act and the Digital Services Act, among others.
author = {Alejandro Saucedo},
title = {{MLOps} 2025: A Journey into the Past and the Future},
year = {2025},
address = {Dublin},
publisher = {USENIX Association},
month = oct
}