Presentation: Panel: ML for Developers/SWEs

Track: Machine Learning for Developers

Location: Soho Complex, 7th fl.

Duration: 11:50am - 12:40pm

Day of week: Monday

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Abstract

Throughout the day, we'll have speakers cover how they've adopted applied machine learning to software engineering. The day wraps with a discussion from the speakers on taking an applied, pragmatic approach to adding ML to your systems and how they solved challenges. Eager to deploy ML and have questions? This is a forum to discuss, learn, and help crystalize that roadmap. Join us discussing first principles adding ML to your systems.

Speaker: Hien Luu

Engineering Leader @LinkedIn - AI & Big Data Enthusiast

Hien Luu is an engineering manager at LinkedIn and he is an AI & big data enthusiast. He is particularly passionate about the intersection between Big Data and Artificial Intelligence. Teaching is one his passions and he is currently teaching Apache Spark course at UCSC Silicon Valley Extension school. He was the author of the "Beginning Apache 2" book, which was published in 2018. He has given presentations at various conferences like QCon (SF, London, Shanghai), Hadoop Summit, JavaOne, and Seattle Data Day.

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Speaker: Jeff Smith

Engineering Manager @Facebook AI

Jeff Smith is an engineering manager at Facebook AI where he supports the PyTorch team. He’s the author of Machine Learning Systems and Exploring Deep Learning for Language. While working at the intersection of functional programming, distributed systems, and machine learning, he coined the term reactive machine learning to describe an ideal machine learning architecture and associated set of techniques. Prior to joining Facebook, he built teams and technology for AI products like x.ai and Amelia.

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Speaker: Brad Miro

Machine Learning Engineer @Google

Brad is passionate about educating the world about artificial intelligence both by empowering developers and improving societal understanding. He is currently a Developer Programs Engineer at Google where he specializes in machine learning and big data solutions. Outside of work, Brad can be found singing, climbing, playing board games and locating the best restaurants in NYC.

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Speaker: Zi Wang

Leading the Machine Learning Engineering Work for Time Predictions @UberEats

Zi used to work on multiple people online collaboration and co-authoring for Office apps at Microsoft.  After joining Uber in 2015, he’s participated in the design and implementation for multiple products, such as Uber Rush, Uber’s in-house payment system, and Uber Eats. Now he’s leading the machine learning engineering work for time predictions in Uber Eats to improve system efficiency and user experience.

Find Zi Wang at

Tracks

Monday, 24 June

Tuesday, 25 June

Wednesday, 26 June