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Alejandro Saucedo

Speaker

Alejandro Saucedo

2

talks

Director of Eng, Science & Product Zalando SE

Alejandro Saucedo is the Director of Engineering, Science & Product at Zalando SE, overseeing a portfolio of 10+ products and platforms, including Zalando's central data platform that handles petabytes of data and several state-of-the-art ML systems powering critical use cases. He is Chief Scientist at the Institute for Ethical AI, contributing to EU policy initiatives such as the AI Act, Data Act, and Digital Services Act. He also runs The ML Engineer newsletter (60k+ subscribers) and serves as AI Expert at the United Nations, Chair of the ML Security Committee at the Linux Foundation, and Chair of the AI Committee at the ACM.

Bio from: Data + AI Summit 2025

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Talks & appearances

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The Future of Real Time Insights with Databricks and SAP

Tired of waiting on SAP data? Join this session to see how Databricks and SAP make it easy to query business-ready data—no ETL. With Databricks SQL, you’ll get instant scale, automatic optimizations, and built-in governance across all your enterprise analytics data. Fast and AI-powered insights from SAP data are finally possible—and this is how.

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 our machine learning pipelines are reliable and scalable. In this talk we dive into the state of production machine learning in the Python Ecosystem, and we will cover the concepts that make production machine learning so challenging, as well as some of the recommended tools available to tackle these challenges.

This talk will cover key principles, patterns and frameworks around the open source frameworks powering single or multiple phases of the end-to-end ML lifecycle, incluing model training, deploying, monitoring, etc. We will be covering a high level overview of the production ML ecosystem and dive into best practices that have been abstracted from production use-cases of machine learning operations at scale, as well as how to leverage tools to that will allow us to deploy, explain, secure, monitor and scale production machine learning systems.