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In addition to being a thriving academic discipline, machine learning is increasingly adopted as a solution to real world business problems. But underneath this seeming success lies a chasm of failures. In fact, according to recent surveys among industry practitioners, the majority of ML projects still fail. It turns out that deployment workflow of ML is far from trivial, and adds a pile of its own challenges on top of those that already exist in software development practice. In this talk we will follow the steps of the ML deployment process, talk about issues people face at each step, and touch upon possible solutions.
The talk is based on this paper, reading it before the talk isn’t required.
This talk will be approximately 45 minutes, leaving 15 minutes for questions/discussion.
This session will take place on Google Meet and participants can join 15 minutes before the start of the session.
We also have a Google Jam Board where you can note down any questions or comments before or during the event.
Andrei is a PhD student in the ML@CL group led by Prof. Neil Lawrence. He is interested in machine learning deployment and how it can be improved with better software systems. Before starting his PhD journey Andrei was developing software for a decade, working on everything from web applications to data center infrastructure to ML applications.
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