Highlights from TensorFlow World 2019
Experts explore TensorFlow 2.0's machine learning capabilities as well as the broader tools and applications of TensorFlow.
AI & ML
Few technologies have the potential to change the nature of work and how we live as artificial intelligence (AI) and machine learning (ML).
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.
“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.
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.js: Bringing machine learning to JavaScript
Sandeep Gupta and Joseph Paul Cohen introduce the TensorFlow.js library.
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.
MLIR: Accelerating AI
Chris Lattner and Tatiana Shpeisman explain how MLIR addresses the complexity caused by software and hardware fragmentation.
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.
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.
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 World 2019 opening keynote
Jeff Dean explains why Google open-sourced TensorFlow and discusses its progress.
TensorFlow community announcements
Kemal El Moujahid reveals new developments for the TensorFlow community.
Highlights from O'Reilly AI London 2019
Experts explore AI's most promising developments, emerging technologies, and profitable use cases.
When to trust AI
Marta Kwiatkowska provides an overview of techniques being developed to help improve the robustness, safety, and trust in AI systems.
When flying is cheaper than standing still
Raffaello D’Andrea presents his vision of how autonomous indoor drones will drive the next wave of robotics development.
Machine learning challenges at LinkedIn: Spark, TensorFlow, and beyond
Zhe Zhang provides an architectural overview of LinkedIn’s machine learning pipelines.
Accelerate with purpose
Walter Riviera discusses three key shifts in the AI landscape.
The quest for high-quality data
Ihab Ilyas describes the HoloClean framework, a prediction engine for structured data with direct applications in detecting and repairing data errors.
Real-time AI for entity resolution
Jeff Jonas details how you can use a purpose-built real-time AI to gain new insights and make better decisions faster.
The power of knowledge at scale
Alexis Crowell Helzer outlines a practical approach to implementing machine learning.
Large-scale machine learning at Facebook: Implications of platform design on developer productivity
Kim Hazelwood and Mohamed Fawzy look at how applied ML has changed the platforms and infrastructure at Facebook.
Building and deploying AI systems at scale
Ben Lorica and Roger Chen review how companies are building AI applications today.
Machine learning for operational analytics and business intelligence
The O’Reilly Data Show Podcast: Peter Bailis on data management, ML benchmarks, and building next-gen tools for analysts.