Presented By
O’Reilly + Cloudera
Make Data Work
29 April–2 May 2019
London, UK

Mass production of AI solutions

Nate Keating (Google)
17:2518:05 Wednesday, 1 May 2019
Data Engineering and Architecture
Location: Capital Suite 8/9
Secondary topics:  Data Platforms
Average rating: ****.
(4.00, 5 ratings)

Who is this presentation for?

  • Executives, data scientists, machine learning engineers, and engineers

Level

Intermediate

Prerequisite knowledge

  • Familiarity with the practical challenges of AI

What you'll learn

  • Learn why scaling the impact of AI will require us to build the equivalent of the moving assembly line for AI

Description

AI will change how we live in the next 30 years, but it’s still currently limited to a small group of companies. In order to scale the impact of AI across the globe, we need to reduce the cost of building AI solutions, but how?

Nate Keating explains how to apply lessons learned from other industries—specifically, the automobile industry, which went through a similar cycle.

The first car was invented in the eighteenth century, and the first electric car was invented in 1888. It took more than a hundred years to mass produce electric cars. Cars become “democratized” when Ford invented the moving assembly line. Ford optimized the manufacturing process, making cars affordable to the middle class.

While AI faces slightly different challenges than the automobile industry, companies like Facebook (FBLearner Flow), Uber (Michelangelo), and Google (TFX) have similarly built internal machine learning “assembly lines” that supercharge AI advancement. Nate explores options and characteristics of those systems as well as the value of a publicly available machine learning assembly line.

Photo of Nate Keating

Nate Keating

Google

Nate Keating is a product manager at Google working on the Cloud AI Platform, which includes Cloud Training and Prediction, AI Hub, Kubeflow, Kubeflow Pipelines, and more to come. Previously, he was manager of IBM’s Applied AI team, which built first-of-a-kind AI solutions for large and strategic clients across all industry verticals and problem spaces. He holds a degree in economics and finance from the Duke University.