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 for these challenges, Databricks unveiled last year MLflow, an open source project that aims at simplifying 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.
In the past year, the MLflow community has grown quickly: over 120 contributors from over 40 companies have contributed code to the project, and over 200 companies are using MLflow.
In this tutorial, we will show you how using MLflow can help you:
We will demo the building blocks of MLflow as well as the most recent additions since the 1.0 release.
What you will learn:
Databricks Solutions Architect and ex-McKinsey Machine Learning Engineer focused on productionizing machine learning at scale.
Databricks Solutions Architect and ex-Teradata Data Engineer with focus on operationalizing Machine Learning workloads in cloud.