Using AutoML to automate selection of machine learning models and hyperparameters / Francesca Lazzeri, Wee Hyong Tok.

Format:

Conference/Event

Video

Author/Creator:

Lazzeri, Francesca, on-screen presenter.

Contributor:

Tok, Wee-Hyong, on-screen presenter.

Conference Name:

O'Reilly Artificial Intelligence Conference (2019 : New York, New York)., issuing body.

Language:

English

Subjects (All):

Machine learning.

Electronic data processing.

Physical Description:

1 online resource (1 streaming video file (42 min., 33 sec.)) : digital, sound, color

Other Title:

Using Auto Machine Learning to automate selection of machine learning models and hyperparameters

Place of Publication:

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

Summary:

"Automated machine learning (AutoML) enables both data scientists and domain experts (with limited machine learning training) to be productive and efficient. In recent years, AutoML has fostered a fundamental shift in how organizations approach machine learning, making it more accessible to both experts and nonexperts. Most real-world data science projects are time-consuming, resource intensive, and challenging. Besides data preparation, data cleaning, and feature engineering, data scientists often spend a significant amount of time on model selection and tuning of hyperparameters. Automated machine learning changes that, making it easier to build and use machine learning models in the real world. Francesca Lazzeri and Wee Hyong Tok (Microsoft) lead a gentle introduction to how AutoML works and the state-of-art AutoML capabilities that are available. You'll learn how to use AutoML to automate selection of machine learning models and automate tuning of hyperparameters."--Resource description page.

Participant:

Presenters, Francesca Lazzeri, Wee Hyong Tok.

Notes:

Title from title screen (viewed November 14, 2019).

Recorded at the 2019 O'Reilly Artificial Intelligence Conference in New York.

OCLC:

1127651203