Highlights from TensorFlow World in Santa Clara, California 2019

Experts explore TensorFlow 2.0's machine learning capabilities as well as the broader tools and applications of TensorFlow.

By Mac Slocum
October 31, 2019
TensorFlow World 2019

People from across the TensorFlow community came together in Santa Clara, California for TensorFlow World. Below you’ll find links to highlights from the event.

Opening keynote

Jeff Dean explains why Google open-sourced TensorFlow and discusses its progress.

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Accelerating ML at Twitter

Theodore Summe offers a glimpse into how Twitter employs machine learning throughout its product.

The latest from TensorFlow

Megan Kacholia explains how Google’s latest innovations provide an ecosystem of tools for developers, enterprises, and researchers who want to build scalable ML-powered applications.

TensorFlow community announcements

Kemal El Moujahid reveals new developments for the TensorFlow community.

TFX: An end-to-end ML platform for everyone

Konstantinos Katsiapis and Anusha Ramesh dive into the insights and approach that helped TensorFlow Extended (TFX) reach its current popularity within Alphabet.

Personalization of Spotify Home and TensorFlow

Tony Jebara explains how Spotify improved user satisfaction by building components of the TFX ecosystem into its core ML infrastructure.

TensorFlow Hub: The platform to share and discover pretrained models for TensorFlow

Mike Liang discusses TensorFlow Hub, a platform where developers can share and discover pretrained models and benefit from transfer learning.

“Human error”: How can we help people build models that do what they expect

Anna Roth discusses human and technical factors and suggests future directions for training machine learning models.

TensorFlow Lite: ML for mobile and IoT devices

Jared Duke and Sarah Sirajuddin explore on-device machine learning and the latest updates to TensorFlow Lite.

Sticker recommendations and AI-driven innovations on the Hike messaging platform

Ankur Narang discusses sticker recommendations with multilingual support, a key innovation driven by sophisticated natural language processing (NLP) algorithms.

TensorFlow.js: Bringing machine learning to JavaScript

Sandeep Gupta and Joseph Paul Cohen introduce the TensorFlow.js library.

MLIR: Accelerating AI

Chris Lattner and Tatiana Shpeisman explain how MLIR addresses the complexity caused by software and hardware fragmentation.

Post topics: AI & ML
Post tags: Signals, TensorFlow World 2019

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