Alejandro Saucedo
Alejandro is Director of Engineering, Science, Product & Analytics at Zalando where he leads a cross-functional technology organisation consisting of department heads, managers, principals and ICs responsible for the development of a large portfolio of (10+) products, the management of one of Zalando's large-scale central data platforms, and the productionisation of SOtA machine learning systems powering high-value & critical use-cases across the organisation. Alejandro is also the Chief Scientist at the Institute for Ethical AI & Machine Learning, where he contributes to policy and industry standards on the responsible design, development and operation of AI, and has led policy contributions including the EU's AI Regulatory Proposal, the Data Act, between others. With over 10 years of software development experience, Alejandro has held technical leadership positions across hyper-growth scale-ups and tech giants, with a strong track record of building cross-functional R&D and Product organisations. He is currently appointed as governing council Member-at-Large at the Association for Computing Machinery (ACM), and is currently the Chairperson of the ML Security Committee at the Linux Foundation.
Linkedin: https://linkedin.com/in/axsaucedo
Twitter: https://twitter.com/axsaucedo
Github: https://github.com/axsaucedo
Website: https://ethical.institute/
Sessions
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, 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.