A data/ML system in production is different from both traditional software engineering and traditional data science/analytics workflows. These differences can be pretty subtle, and trying to use your traditional skill sets to solve these new problems doesn’t work. Ewan demonstrates a realistic machine learning system in production and uses this demo to show some patterns that he has found useful for living with ML in production, and maybe debunk a couple of myths along the way. Ewan also points at some MLOps developments that he really likes and shows some things on his wish list.
In this episode
Head of Data Science, Forecast
Ewan graduated in 2006 with a BSc in Physics. Since then he's built a career in data. He has a breadth of experience in technical roles, covering the fields of data science, analytics, and data engineering. He's also an experienced leader in the field, with a particular focus on coaching and team development, and culture. Before joining Forecast he's worked for companies including the BBC and Skyscanner and found himself in industries including seismic exploration, technology, and advertising. When he's not crunching numbers Ewan is a bit of a bookworm, enjoys traveling, watching cricket, and getting out and about in the Scottish Highlands.
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.