• Simplifying Production Machine Learning with MLflow

    1 Richmond St W

    Building and deploying a machine learning model can be difficult to do once. Enabling other engineers (or yourself, one month later) to reproduce your pipeline, to compare the results of different versions, to track what’s running where, and to redeploy and rollback updated models is much harder. In this talk, I’ll introduce MLflow, a new open-source project launched by Databricks that simplifies the machine learning lifecycle. MLflow provides APIs for tracking experiment runs between multiple users, reproducing work, and deploying and managing models. MLflow is designed to be an open, modular platform that you can use it with any existing ML library and development process. MLflow was launched in June 2018 and has already seen significant community contributions, with over 130 contributors to date, including teams from Microsoft and R Studio. I’ll give special focus to new components in MLflow including the MLflow Model Registry and auto-logging packages for TensorFlow and Keras. Matei Zaharia - https://www.linkedin.com/in/mateizaharia/ Matei is an Assistant Professor of Computer Science at Stanford University and Chief Technologist at Databricks. He started the Apache Spark project during his PhD at UC Berkeley in 2009, and has worked broadly on datacenter systems, co-starting the Apache Mesos project and contributing as a committer on Apache Hadoop. Today, Matei tech-leads the MLflow development effort at Databricks. Matei’s research work was recognized through the 2014 ACM Doctoral Dissertation Award for the best PhD dissertation in computer science, an NSF CAREER Award, and the US Presidential Early Career Award for Scientists and Engineers. Schedule: 6:30 PM - 7:00 PM - Sign-up and Mingling 7:00 PM - 8:00 PM - "Simplifying Production Machine Learning with MLflow" 8:00 PM - 8:30 PM - Q & A