Machine learning and AI models now outperform humans on tasks ranging from image recognition to language translation. However, sending video, audio, and other sensor data up to the cloud and back is too slow for apps like Snapchat, features like “Hey, Siri!,” and autonomous machines like self-driving cars. Developers seeking to provide seamless user experiences must now move their models down to devices on the edge of the network where they can run faster, at lower cost, and with greater privacy.
Jameson Toole details why developers should be deploying deep learning models to the edge, common roadblocks they will face, and how to overcome them. Jameson begins by describing two major technological trends pushing machine learning out to the edge of the network: the rise of deep learning and the ubiquity of mobile sensors. He then explains how developers can use tools like Core ML and TensorFlow Lite to solve problems at 60 fps and create smooth, real-time experiences for users—all while securing data and reducing cloud costs.
Despite these benefits, transitioning to the edge can be difficult. Tech stacks used by machine learning specialists and mobile developers are mismatched, and it’s rare to find engineers fluent in both. Processing power, storage, and memory are all constrained, and developers need to ensure their models can run on hundreds of different devices. Join Jameson to get advice on how to deal with these challenges so that your migration to the edge is as simple and painless as possible.
Jameson Toole is the cofounder and CEO of Fritz AI, a company building tools to help developers optimize, deploy, and manage machine learning models on mobile devices. Previously, he built analytics pipelines for Google X’s Project Wing and ran the data science team at Boston technology startup Jana Mobile. He holds undergraduate degrees in physics, economics, and applied mathematics from the University of Michigan and both an MS and PhD in engineering systems from MIT, where he worked on applications of big data and machine learning to urban and transportation planning at the Human Mobility and Networks Lab.
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