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Speeding Up ML Development with MLFlow

Machine Learning is more approachable than ever before and the number of companies applying Machine Learning to build AI powered applications and products has dramatically increased in recent years.  On this journey of adopting Machine Learning, many companies learn successful Machine Learning projects require good software infrastructure to enable quick experiment iteration, ease of model development and deployment. Some of these large companies have sufficient resources to invest in building the necessary software infrastructure for their needs and the rest of the companies are looking for open source solutions to help them.  

MLflow, an open source platform for the Machine Learning development lifecycle, was created in 2018 to simplify and speed up the development of AI powered applications. It was designed to be extensible and pluggable from day one.  

This session will share the common needs in the Machine Learning development lifecycle and how MLflow can satisfy some of those needs, and it will end with a demo.


Hien Luu

Engineering Manager @LinkedIn focused on Big Data

Hien Luu is a technical lead of the Data Services Platform team at LinkedIn where he focuses on building big data infrastructure and big data applications. He loves working with big data technologies and recently became a contributor of Apache Pig project. He enjoys teaching and is currently an...

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Whittle, 3rd flr.


Machine Learning: The Latest Innovations


Silicon ValleyMachine LearningScalabilityDeployment


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