Model Watching: Keeping Your Project in Production // Ben Wilson // MLOps Meetup #58

MLOps community meetup #58! Last Wednesday we talked to Ben Wilson, Practice Lead Resident Solutions Architect, Databricks. Model Monitoring Deep Dive with the author of Machine Learning Engineering in Action. It was a pleasure getting to talk to Ben about difficulties in monitoring in machine learning. His expertise obviously comes from experience and as he said a few times in the meetup, I learned the hard way over 10 years as a data scientist so you don't have to! Ben was also kind enough to give us a 35% off promo code for his book! Use the link: http://mng.bz/n2P5 //Abstract 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. //Bio 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. //Takeaways Understanding why attribution and performance monitoring is critical for long-term project success Borrowing hypothesis testing, stratification for latent confounding variable minimization, and statistical significan