Presented By O’Reilly and Intel AI
Put AI to work
April 10-11, 2018: Training
April 11-13, 2018: Tutorials & Conference
Beijing, CN

Turning machine learning research into products for industry

This will be presented in English.

Reza Zadeh (Matroid | Stanford)
09:3009:45 Thursday, April 12, 2018
Secondary topics:  计算机视觉(Computer Vision)


Reza Zadeh details three challenges on the way to building cutting-edge ML products, with a focus on computer vision, offering examples, recommendations, and lessons learned.

  1. Since ML is all about approximation, it can be difficult to assess when a research result is good enough for the industry.
  2. Building systems that scale ML models in production is a challenge on its own. Although a model may work in the lab, scaling it to millions of users may be impossible without further research.
  3. Building good user interfaces for ML products is crucial. Since ML researchers often don’t have a background in user-focused design, they tend to underestimate the importance of good UX design.


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.

Comments on this page are now closed.


HW Yang | PROF
2018-04-12 14:18 CST

1. As the ML is based on probility, what is the big prob when it would be used to predict the real model in business or science? And its reliability?
2. Cons and Pros of ML, and future development