AI as an Engineering Discipline


The field of Artificial Intelligence (AI) has transformed over the last few decades, and has evolved from a deeply mathematical and theoretical discipline into a software engineering discipline. For the first time in history, AI is truly accessible. However, an open question is what is the right way to use AI? What are the engineering best practices around AI? In this talk we first briefly discuss how modern AI came about and how it has changed the rules of Machine Learning development. Then we will try to establish some new guidelines and engineering principles. that will allow you to cut through the noise of AI tooling, and assist in determining what is most effective for your tasks.


Yucheng Low has a PhD in Machine Learning from Carnegie Mellon University where he worked on large scale distributed probabilistic graphical model inference. In 2013, he joined in with his PhD advisor Prof. Carlos Guestrin to start a ML company called GraphLab which was then renamed twice to Dato and Turi. At GraphLab/Dato/Turi, Yucheng was the Chief Architect and helped build scalable single machine DataFrame and ML algorithms. Turi was acquired by Apple in 2016. At Apple, he worked on different parts of the ML platform stack ranging from storage to inference. In 2021 he left Apple and together with a couple of Apple colleagues, started XetHub, and is now developing the next generation of storage technology for AI/ ML collaboration. In 2023, his paper on Distributed GraphLab won the VLDB Test of Time award.

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