Executive briefing : usable machine learning - lessons from Stanford and beyond / Peter Bailis.

Format:

Video

Author/Creator:

Bailis, Peter, on-screen presenter.

Language:

English

Subjects (All):

Machine learning.

Artificial intelligence.

O'Reilly Artificial Intelligence Conference (2019 : San Jose, California).

O'Reilly Artificial Intelligence Conference.

Physical Description:

1 online resource (1 streaming video file (48 min., 30 sec.)) : digital, sound, color

Other Title:

Usable machine learning - lessons from Stanford and beyond

Place of Publication:

[Place of publication not identified] : O'Reilly Media, 2019.

Summary:

"Despite a meteoric rise in data volumes within modern enterprises, enabling nontechnical users to put this data to work in diagnostic and predictive tasks remains a fundamental challenge. Peter Bailis (Sisu | Stanford University) details the lessons learned in building new systems and interfaces to help users quickly and easily leverage the data at their disposal with production experience from Facebook, Microsoft, and the Stanford DAWN project. Drawing on his research and startup experience, Peter examines why deep networks aren't a panacea for most organizations' data; how usability and speed are the best path to better models; why Facebook, Apple, Amazon, Netflix, and Google (FAANG) likely won't (and can't) dominate every vertical; and why automating feature selection is more practical than AutoML. This session is from the 2019 O'Reilly Artificial Intelligence Conference in San Jose, CA."--Resource description page.

Participant:

Presenter, Peter Bailis.

Notes:

Title from title screen (viewed July 22, 2020).

OCLC:

1177143004