Put AI to work
June 26-27, 2017: Training
June 27-29, 2017: Tutorials & Conference
New York, NY

Scaling computer vision in the cloud

Reza Zadeh (Matroid | Stanford)
1:45pm2:25pm Thursday, June 29, 2017
Implementing AI
Location: Sutton South/Regent Parlor Level: Intermediate
Secondary topics:  Cloud, Deep Learning, Vision

Prerequisite Knowledge

  • A basic understanding of convolutional neural networks

What you'll learn

  • Learn how Matroid put together TensorFlow, Kubernetes, and Amazon Web Services for computer vision


Providing customized computer vision solutions to a large number of users is a challenge. Matroid allows the creation and serving of computer vision models and algorithms, model sharing between users, and serving infrastructure at scale.

Reza Zadeh offers an overview of Matroid’s pipeline, which uses TensorFlow, Kubernetes, and Amazon Web Services, and explains how Matroid allows customization of computer vision neural network models in the browser, followed by building, training, and visualizing TensorFlow models, which are provided at scale to monitor streams of video and search static video.

Photo of Reza Zadeh

Reza Zadeh

Matroid | Stanford

Reza Bosagh Zadeh is founder and CEO at Matroid and an adjunct professor at Stanford University, where he teaches two PhD-level classes: Distributed Algorithms and Optimization and Discrete Mathematics and Algorithms. His work focuses on machine learning, distributed computing, and discrete applied mathematics. His awards include a KDD best paper award and the Gene Golub Outstanding Thesis Award. Reza has served on the technical advisory boards of Microsoft and Databricks. He is the initial creator of the linear algebra package in Apache Spark. Through Apache Spark, Reza’s work has been incorporated into industrial and academic cluster computing environments. Reza holds a PhD in computational mathematics from Stanford, where he worked under the supervision of Gunnar Carlsson. As part of his research, Reza built the machine learning algorithms behind Twitter’s who-to-follow system, the first product to use machine learning at Twitter.