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.
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.
Jeff Dean explains why Google open-sourced TensorFlow and discusses its progress.
- Watch “Opening keynote“
Accelerating ML at Twitter
Theodore Summe offers a glimpse into how Twitter employs machine learning throughout its product.
- Watch “Accelerating ML at Twitter“
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.
- Watch “The latest from TensorFlow“
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.
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.
- Watch “MLIR: Accelerating AI“