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 and it was designed to be extensible and pluggable from day one to simplify and speed up the the development of AI powered applications.
This session is designed to share the common needs in the Machine Learning development lifecycle, an overview of the features in the MLflow platform and it will end with a demo.
Engineering manager at LinkedIn and an instructor at UCSC Extension school.