Managing the Complete Machine Learning Lifecycle with MLflow
Managing the Complete Machine Learning Lifecycle with MLflow

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.

Jules Damji walks you through MLflow, an open source project that simplifies the entire ML lifecycle, to solve this problem. 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.

What you'll learn
Understand the three main components of open source MLflow (MLflow Tracking, MLflow Projects, and MLflow Models) and how each help address challenges of the ML lifecycle
Learn how to use MLflow Tracking to record and query experiments (code, data, config, and results), how to use MLflow Projects packaging format to reproduce runs, and how to use MLflow Models general format to send models to diverse deployment tools

Bio: 

Jules S. Damji is an Apache Spark community and developer advocate at Databricks. He’s a hands-on developer with over 20 years of experience. Previously, he worked at leading companies such as Sun Microsystems, Netscape, @Home, LoudCloud/Opsware, Verisign, ProQuest, and Hortonworks, building large-scale distributed systems. He holds a BSc and MSc in computer science and MA in political advocacy and communication from Oregon State University, the California State University, and Johns Hopkins University, respectively.

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