This event will be Livestreamed here:
https://livestream.com/accounts/23925505/events/8520690

Deploying machine learning models can be a pain point for many engineering teams. There are multiple avenues for success for a particular project but there can also be roads to disaster.

In this talk, I will discuss how to deploy Driverless AI pipelines. Topics will include the advantages & disadvantages for each deployment option - for example, why you might want to communicate to your scoring service over HTTP instead of using the language-level API. In addition, I will discuss general best practices that I have seen work & leave time to discuss your production questions - or new theories you now have as a result of the talk.

About Justin Loyola

Justin has been a Software Engineer at H2O.ai since 2016 working on Steam, H2O, & Driverless AI. Previously he worked at Captora, a marketing automation software company, & Google. He has focused on web development & has experience working on infrastructure operations, development operations, API development, & UI work. He earned his Bachelor's degree in History from UCSD & his Juris Doctorate from the University of Denver.

About Metis

Metis (thisismetis.com) accelerates careers in data science by providing full-time immersive bootcamps, evening part-time professional development courses, online resources, & corporate programs based in Seattle, New York, Chicago, & San Francisco.

Brought to you by Kaplan, Metis focuses primarily on Python, machine learning, data visualization, deep learning, big data processing, statistical foundations, & more. Students & alumni of the bootcamp program receive continuous support from our career advisors, empowering them to pursue a successful career in the fast-growing field of data science.

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