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

Risks, hidden costs, and how to escape the black hole of machine learning technical debt

Matt Zeiler (Clarifai)
2:35pm3:15pm Thursday, June 29, 2017
Implementing AI
Location: Grand Ballroom West Level: Intermediate
Secondary topics:  Cloud, Deep Learning, Machine Learning, Vision
Average rating: *....
(1.33, 3 ratings)

Prerequisite Knowledge

  • A basic understanding of machine learning

What you'll learn

  • Learn different options for effectively building and scaling AI into your business
  • Understand how machine learning increases technical debt and ways to mitigate it

Description

Pretty much every technology company is aware of the concept of technical debt, but when technology companies discuss technical debt, they primarily refer to technical debt related to code. However, technical debt requires a broader definition, especially when it comes to building artificial intelligence-powered machine learning systems and applications. AI-powered machine learning technologies bring a higher and more complex level of technical debt to applications, especially if the AI and machine learning system has been built from the ground up.

Matt Zeiler shares best practices for companies hoping to build AI into their businesses and explores how machine learning increases technical debt, the key contributors to machine learning-specific debt, and how to avoid or reduce technical debt related to machine learning.

Photo of Matt Zeiler

Matt Zeiler

Clarifai

Matthew Zeiler is the founder and CEO of Clarifai, where he is applying his award-winning research to create the best visual recognition solutions for businesses and developers and power the next generation of intelligent apps. An artificial intelligence expert, Matt led groundbreaking research in computer vision, alongside renowned machine learning pioneers Geoff Hinton and Yann LeCun, that has propelled the image recognition industry from theory to real-world practice. He holds a PhD in machine learning from NYU.

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