Workshop Sessions: Complete ML Lifecycle with MLflow - Learn it's Four Components
Speaker Bio:
Jumes Damji, Senior Developer Advocate at Databricks
Jules S. Damji is a Senior Developer Advocate at Databricks, an MLflow contributor, and O’Reilly co-author of Learning Spark 2nd. He is a hands-on developer with over 20 years of experience and has worked at leading companies, such as Sun Microsystems, Netscape, @Home, Opsware/Loudcloud, VeriSign, ProQuest, and Hortonworks, building large-scale distributed systems. He holds a B.Sc and M.Sc in Computer Science (from Oregon State University and Cal State, Chico respectively), and an MA in Political Advocacy and Communication (from Johns Hopkins University).
Abstract:
ML development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models.
To solve these challenges, MLflow, an open source project, simplifies the entire ML lifecycle. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organizations of any size.