A great deal of time is spent building out the most effectively tuned model, production-hardened code, and elegant implementation for a business problem. Shipping our precious and clever gems to production is not the end of the solution lifecycle, though, and many-an-abandoned projects can attest to this. In this talk, we will discuss how to think about model attribution, monitoring of results, and how (and when) to report those results to the business to ensure a long-lived and healthy solution that actually solves the problem you set out to solve.
In this episode
Practice Lead Resident Solutions Architect, Databricks
Ben Wilson has worked as a professional data scientist for more than ten years. He currently works as a resident solutions architect at Databricks, where he focuses on machine learning production architecture with companies ranging from 5-person startups to global Fortune 100. Ben is the creator and lead developer of the Databricks Labs AutoML project, a Scala-and Python-based toolkit that simplifies machine learning feature engineering, model tuning, and pipeline-enabled modeling. He's the author of Machine Learning Engineering in Action, a primer on building, maintaining, and extending production ML projects.
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.