Presented By O'Reilly and Cloudera
Make Data Work
March 13–14, 2017: Training
March 14–16, 2017: Tutorials & Conference
San Jose, CA

The state of TensorFlow today and where it is headed in 2017

Rajat Monga (Google)
11:00am11:40am Thursday, March 16, 2017
Secondary topics:  AI, Deep learning
Average rating: ***..
(3.67, 6 ratings)

What you'll learn

  • Explore TensorFlow and get a glimpse of where it is headed

Description

In 2016, TensorFlow went from being an early release to the most popular machine-learning library. With strong distributed support, improved performance, and wider platform coverage (and more added through the year), it has seen widespread adoption across the industry.

Rajat Monga offers an overview of TensorFlow progress and adoption in 2016 before looking ahead to the areas of importance in the future—performance (speed, scale, and power—important components for the success of machine learning), usability (higher-level libraries and packages to make machine learning easier for more people to use), and ubiquity (machine-learning models that can run everywhere, from the cloud to the edge)—and the efforts TensorFlow is making in those areas.

Photo of Rajat Monga

Rajat Monga

Google

Rajat Monga leads TensorFlow, an open source machine-learning library and the center of Google’s efforts at scaling up deep learning. He is one of the founding members of the Google Brain team and is interested in pushing machine-learning research forward toward general AI. Previously, Rajat was the chief architect and director of engineering at Attributor, where he led the labs and operations and built out the engineering team. A veteran developer, Rajat has worked at eBay, Infosys, and a number of startups.

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Comments

Michael Corwin | DIRECTOR OF PLATFORM ENGINEERING
03/22/2017 1:34am PDT

Are the slides available?