The State of Production Machine Learning in 2024 // Alejandro Saucedo // AI in Production

// Abstract
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 robust and scalable. In this talk we dive into the state of production machine learning in 2024, 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. We will be covering a deep dive of the production ML tooling 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.
// Bio
Alejandro is the Director of Engineering, Science & Product at Zalando SE, where he is responsible for a large portfolio of (10+) products and platforms, including one of Zalando's petabyte-scale central data platforms, and several State-of-the-Art machine learning systems (Forecasting, Causal Inference & NLP) powering critical use-cases across the organisation. He is also the Chief Scientist at the Institute for Ethical AI, where he contributes to policy and standards on the responsible design, development and operation of machine learning systems. Alejandro is currently the Chair of the ML Security Committee at the Linux Foundation, and Chair of the AI Committee at the Association for Computing Machinery (ACM), where he has led EU policy contributions across the AI Act, the Data Act and the DSA, between others.
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