We started out talking about some of the main bottlenecks they have encountered over the years of trying to push data products into production environments. Then things started to heat up as we dove into the topic of monitoring ML and inevitably the word explainability started being thrown around. Turns out Lex is currently doing a Ph.D. on the subject so there was much to talk about. We had to ask if explainability is now table-stakes when it comes to monitoring solutions on the market? The short answer from the team. Yes! Please excuse the bit of sound trouble we had with google mike at the beginning.
In this episode
ML Engagement Lead, Spotify
Lex Beattie is the Machine Learning Engagement Lead at Spotify. In the last year, she has helped over 40 different teams across Spotify understand ML best practices, productionize ML workflows and implement impactful ML in their products. Lex is also a Ph.D. candidate at the University of Oklahoma, focusing on feature importance and interpretability in deep neural networks. Beyond her passion for all things ML, she enjoys exploring the great outdoors in Montana with her German Wirehaired Pointer, Bridger.
ML Solutions Engineer, Google
Michael is an ML Solutions Engineer with Google Cloud and Google's Advanced Solutions Lab. In his role, he works with customers to build and deploy end-to-end ML solutions with Google Cloud. Within the Advanced Solutions Lab, he teaches these skills to customers.
Principal Engineer, Skyscanner
Mike has worked across many dimensions; in large/tiny companies, back-end/front-end, with many languages, and as a sys-admin /engineer/manager. Mike has a healthy skepticism for most things and likes solving problems through applying System thinking.
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.