Presented By
O’Reilly + Cloudera
Make Data Work
March 25-28, 2019
San Francisco, CA

MLflow: An open platform to simplify the machine learning lifecycle

Corey Zumar (Databricks)
4:20pm5:00pm Wednesday, March 27, 2019
Secondary topics:  Model lifecycle management
Average rating: ****.
(4.89, 9 ratings)

Who is this presentation for?

  • Data scientists and engineers working on machine learning infrastructure

Level

Intermediate

Prerequisite knowledge

  • Familiarity with machine learning (useful but not required)

What you'll learn

  • Understand why developing machine learning applications at scale is difficult
  • Learn how MLflow provides a solution to ease development at all points of the machine learning lifecycle
  • Discover how to get started with MLflow and contribute to the project

Description

Developing applications that successfully leverage machine learning is difficult. Building and deploying a machine learning model is challenging to do once. Enabling other data scientists (or even yourself, one month later) to reproduce your pipeline, compare the results of different versions, track what’s running where, and redeploy and rollback updated models is much harder.

Corey Zumar offers an overview of MLflow, a new open source project from Databricks that simplifies this process. MLflow provides APIs for tracking experiment runs between multiple users within a reproducible environment and for managing the deployment of models to production. Moreover, MLflow is designed to be an open, modular platform—you can use it with any existing ML library and incorporate it incrementally into an existing ML development process.

Photo of Corey Zumar

Corey Zumar

Databricks

Corey Zumar is a software engineer at Databricks, where he’s working on machine learning infrastructure and APIs for model management and production deployment. Corey is also an active contributor to MLflow. He holds a master’s degree in computer science from UC Berkeley. At UC Berkeley’s RISELab, he was one of the lead developers of Clipper, an open source project and research effort focused on high-performance model serving.