It's a cliche to say that choosing and running the algorithms is only a small part of a typical ML project but despite that it's true! Setting up and organizing the project, dealing with the data asset, getting to the heart of the business problem, assessing and choosing the models, and integrating them with the business processes in production are all at least as time-consuming and important. Simon has written a book that talks about how these different activities need to be orchestrated and executed and he hopes that it might be useful for people who are starting out managing ML projects and help them avoid some of the crunches and catches that seem to trip people up.
In this episode
Head of Data Science, GFT Group
Simon has been building and running ML projects since 1994 (when he started his Ph.D. in MachineLearning). His first commercial project was for the Royal Navy, and since then he has worked in Telecom, Defense, Consultancy, Manufacturing, and Finance. This means Simon has experienced a wide range of working environments and different types of projects. As well as working in a variety of commercial environments Simon collaborated on EU research projects, UK Government funded research projects and worked as an industrial rep on three MIT consortia (BigData@CSAIL, Systems That Learn, and the CISR Data Research Board). Simon was also an industrial fellow at the Alan Turing Institute for a year. This means that he has also seen a lot of the communities' practices and concerns as they developed, and he had the chance to put them into use in a commercial environment. Right now, Simon is working for a technology consultancy called GFT, and his job there is primarily to deliver ML projects for companies in the capital markets such as investment banks, although we also do work in retail banking, insurance, and manufacturing.
Demetrios is one of the main organizers of the MLOps community and currently resides in a small town outside Frankfurt, Germany. He is an avid traveller who taught English as a second language to see the world and learn about new cultures. Demetrios fell into the Machine Learning Operations world, and since, has interviewed the leading names around MLOps, Data Science, and ML. Since diving into the nitty-gritty of Machine Learning Operations he felt a strong calling to explore the ethical issues surrounding ML. When he is not conducting interviews you can find him making stone stacking with his daughter in the woods or playing the ukulele by the campfire.
Abi is a machine learning engineer and a live streamer. Over the past six years, her focus has been building machine learning models for various industries including short-form video hosting, OTT, e-commerce, insurance tech, etc., for startups across the US, UK, Canada, and India. Prior to that, she was a Data Science Fellow with Insight Toronto and a Visiting Research Scholar at UCLA working in AutoML, MultiAgent Systems, and Emotion Recognition. In her free time, she helps produce the MLOps Community Podcast.