Mar 15–18, 2020

Creating smaller, faster, production-worthy mobile machine learning models

Jameson Toole (Fritz AI)
11:00am11:40am Tuesday, March 17, 2020
Location: 210 B

Who is this presentation for?

  • Developers, data scientists, AI enthusiasts, mobile developers, product designers, and product managers




Getting ML models ready for use on device is a major challenge. Drag-and-drop training tools can get you started, but the models they produce aren’t small enough or fast enough to ship. Jameson Toole explains optimization, pruning, and compression techniques that keep app sizes small and inference speeds high.

There are flexible model architectures that meet performance and accuracy requirements across devices and platforms. You’ll discover pruning and distillation techniques to optimize model performance and use quantization tools to compress models to a fraction of their original size. Along the way, you’ll see a practical example of this process as you create an artistic style transfer model that’s just 17 KB. All of these techniques are applied to mobile ML frameworks like Core ML and TensorFlow Lite.

Prerequisite knowledge

  • Experience with mobile ML frameworks like Core ML or TensorFlow Lite
  • A basic understanding of how neural networks are designed and trained

What you'll learn

  • Learn techniques for optimizing mobile ML models for production, both in terms of size and inference speeds
Photo of Jameson Toole

Jameson Toole

Fritz AI

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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