TensorFlow @ Google I/O ’19 Recap

May 30 · 6 min read

Posted by Marcus Chang, Program Manager

Google I/O ’19 is now a wrap! From May 7–9, there were 13 AI and Machine Learning specific talks at I/O. TensorFlow was well represented with sessions on 2.0, AI for Mobile and IoT Devices, Swift for TensorFlow, TensorFlow Extended, TensorFlow.js, TensorFlow Graphics and much more! This post contains a listing of all the talks, and links.

Recorded sessions are now available to view on the TensorFlow YouTube channel (you can find the entire playlist here).

Machine Learning on Your Device: The Options

Developers have an often confusing plethora of options available to them in using machine learning to enhance their mobile apps and edge devices. This session demystified these options, showing you how TensorFlow can be used to train models and how you can use these models across a variety of devices with TensorFlow Lite.

Getting Started with TensorFlow 2.0

TensorFlow 2.0 is here! This talk will share a few examples for beginners and experts, and cover some of the differences between TensorFlow 1.0 and 2.0.

Swift for TensorFlow

Swift for TensorFlow is a platform for the next generation of machine learning that leverages innovations like first-class differentiable programming to seamlessly integrate deep neural networks with traditional software development. Learn how Swift for TensorFlow can make advanced machine learning research easier and why Jeremy Howard’s fast.ai has chosen it for the latest iteration of their deep learning course.

AI for Mobile and IoT devices: TensorFlow Lite

Imagine building an app that still hears voice commands when your phone is offline, or identifying products in real time with your camera. Learn how to build AI into any device using TensorFlow Lite, and no ML experience is required. Discover a library of pretrained models that are ready to use in your apps, or customize to your needs. You’ll see how quickly you can add ML to Android and iOS apps.

TensorFlow Extended (TFX): ML Pipelines and Model Understanding

This talk focuses on creating a production ML pipeline using TFX. Using TFX developers can implement ML pipelines capable of processing large datasets for both modeling and inference. In addition to data wrangling and feature engineering over large datasets, TFX enables detailed model analysis and versioning. This session focuses on implementing a TFX pipeline and a discussion of current topics in model understanding.

Machine Learning magic for your JavaScript application

TensorFlow.js is a library for training and deploying ML models in the browser and in Node.js, and offers unique opportunities for JavaScript developers. Learn about the TensorFlow.js ecosystem: how to bring an existing ML model into your JS app, re-train the model using your data and go beyond the browser to other JS platforms.

Federated Learning: Machine Learning on decentralized data

Meet federated learning: a technology for training and evaluating machine learning models across a fleet of devices (e.g. Android phones), orchestrated by a central server, without sensitive training data leaving any user’s device. Learn how this privacy-preserving technology is deployed in production in Google products and how TensorFlow Federated can enable researchers and pioneers to simulate federated learning on their own datasets.

Cloud TPU Pods: AI supercomputing that solves large ML problems

Cloud Tensor Processing Unit (TPU) is an ASIC designed by Google for neural network processing. TPUs feature a domain specific architecture designed specifically for accelerating TensorFlow training and prediction workloads and provides performance benefits on machine learning production use. Learn the technical details of Cloud TPU and Cloud TPU Pod, and new features of TensorFlow that enables a large scale model parallelism for deep learning training.

Machine Learning Fairness: Lessons Learned

ML fairness is a critical consideration in machine learning development. This session presented a few lessons Google has learned through our products, research, and how developers can apply these learnings in their own efforts. There’s a walkthrough of techniques that will enable model performance evaluation and improvement, and the resources such as datasets and Tensorflow Model Analysis that are at their disposal. This talk enables developers to proactively think about fairness in product development.

Machine Learning Zero to Hero

This is a talk for people who know code, but who don’t necessarily know ML. Learn the ‘new’ paradigm of machine learning, and how models are an alternative implementation for some logic scenarios, as opposed to writing if/then rules and other code. This recap guides you through understanding many of the new concepts in ML that you might not be familiar with, including eager mode, training loops, optimizers, and loss functions.

TF-Agents: A Flexible Reinforcement Learning Library for TensorFlow

TF-Agents is a clean, modular, and well-tested open-source library for Deep Reinforcement Learning with TensorFlow. This session covered recent advancements in Deep RL, and show how TF-Agents can help to jump start your project. You will also see how TF-Agent library components can be mixed, matched, and extended to implement new RL algorithms.

Cutting Edge TensorFlow: New Techniques

There’s lots of great new things available in TensorFlow since last year’s IO. This recap will take you through 4 of the hottest from Hyperparameter Tuning with Keras Tuner to Probabilistic Programming to being able to rank your data with learned ranking techniques and TF-Ranking. Finally, you will look at TF-Graphics that brings 3D functionalities to TensorFlow.

Introducing Google Coral: Building on-device AI

This session introduced Google Coral, a new platform for on-device AI application development and showcase it’s ML acceleration power with TensorFlow demos. Coral offers you the tools to bring private, fast, and efficient neural network acceleration right onto your device, and enables you to grow ideas of AI application from prototype to production. Learn the technical specs of Edge TPU hardware and software tools, as well as application development process.

TensorFlow.js and TensorFlow Lite also hosted demo stations in the ML/AI sandbox at I/O to showcase what’s new and to answer questions from attendees who visited the dome during the 3-day event!

With TensorFlow.js you can bring the power of ML to your JavaScript applications by using one of many pre-packaged models, start with previously trained models and use transfer learning to customize it on your own data, and deploy in browser, or server-side using Node.js. See some cool demos and examples and tutorials to help you get started.

TensorFlow Lite helps you bring AI to mobile apps and edge devices! To learn more, visit tensorflow.org/lite. Explore our Android and iOS example apps, download our pre-trained, mobile-optimized ML models, or learn about ML on microcontrollers.


TensorFlow is an end-to-end open source platform for machine learning.


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TensorFlow is a fast, flexible, and scalable open-source machine learning library for research and production.


TensorFlow is an end-to-end open source platform for machine learning.