Presentation: Machine Learning on Mobile and Edge Devices With TensorFlow Lite

Track: Living on the Edge: The World of Edge Compute From Device to Infrastructure Edge

Location: Bayview AB

Duration: 1:40pm - 2:30pm

Day of week: Wednesday

Share this on:

Abstract

Machine learning enables some incredible applications, from human-centric user interfaces to generative art. But the traditional machine learning architecture is server-based, with data being sent from users' devices to the cloud, and users are rightly concerned about privacy, safety, and control over their data.  

In this talk, we'll learn how developers can use TensorFlow Lite to build amazing machine learning applications that run entirely on-device. We'll see how running models on-device leads to lower latency, improved privacy, and robustness against connectivity issues. And we'll get familiar with the workflows, tools, and platforms that make on-device inference possible.  

You'll leave this session ready to deploy machine learning models to a wide range of devices, from mobile phones to ultra-low power microcontrollers. You'll learn where to find pre-trained models that can solve a wide range of problems, and how to optimize your own models so they work well on devices.

Speaker: Daniel Situnayake

Developer Advocate for TensorFlow Lite @Google and Co-Author of TinyML

Daniel is an expert in on-device machine learning inference. He currently works on TensorFlow Lite at Google. He’s the co-author of TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low Power Microcontrollers, published by O’Reilly, and leads the Introduction to TensorFlow Lite course from Udacity. He also hosts the monthly tinyML meetup in Silicon Valley.  

Before landing at Google, Dan co-founded Tiny Farms Inc., America’s first insect farming technology company. He’s spent time as a software engineer in Silicon Valley and the financial industry, and was a full-time faculty member at Birmingham City University.

Find Daniel Situnayake at

Similar Talks

CI/CD for Machine Learning

Qcon

Program Manager on the Azure DevOps Engineering Team @Microsoft

Sasha Rosenbaum

ML in the Browser: Interactive Experiences with Tensorflow.js

Qcon

Research Engineer in Machine Learning @cloudera

Victor Dibia

Machine Learning 101

Qcon

Data Scientist @IBM

Grishma Jena

ML/AI Panel

Qcon

Staff Developer Relations Engineer @Google Cloud Platform

Amy Unruh

Tracks

Monday, 11 November

Tuesday, 12 November

Wednesday, 13 November