Presentation: MLflow: An Open Platform to Simplify the Machine Learning Lifecycle

Track: Machine Learning for Developers

Location: Soho Complex, 7th fl.

Duration: 2:55pm - 3:45pm

Day of week: Monday

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Abstract

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.

Speaker: Corey Zumar

Software Engineer @databricks

Corey Zumar is a software engineer at Databricks, where he’s working on machine learning infrastructure and APIs for the machine learning lifecycle, including model management and production deployment. Corey is also an active developer of 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.

Find Corey Zumar at

Tracks

Monday, 24 June

Tuesday, 25 June

Wednesday, 26 June