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Monitoring and improving the performance of machine learning models : how to use ModelDB and Spark to track and improve model performance over time

Author: Manasi Vartak; Jason Slepicka
Publisher: [Place of publication not identified] : O'Reilly, 2017.
Edition/Format:   eVideo : Clipart/images/graphics : EnglishView all editions and formats
Summary:
"It's critical to have 'humans in the loop' when automating the deployment of machine learning (ML) models. Why? Because models often perform worse over time. This course covers the human directed safeguards that prevent poorly performing models from deploying into production and the techniques for evaluating models over time. We'll use ModelDB to capture the appropriate metrics that help you identify poorly  Read more...
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Details

Material Type: Clipart/images/graphics, Internet resource, Videorecording
Document Type: Internet Resource, Computer File, Visual material
All Authors / Contributors: Manasi Vartak; Jason Slepicka
OCLC Number: 1019708044
Notes: Title from title screen (Safari, viewed January 15, 2018).
Release date from resource description page (Safari, viewed January 15, 2018).
Performer(s): Presenter, Manasi Vartak.
Description: 1 online resource (1 streaming video file (35 min., 52 sec.)) : digital, sound, color
Other Titles: How to use ModelDB and Spark to track and improve model performance over time
Responsibility: with Manasi Vartak & Jason Slepicka.

Abstract:

"It's critical to have 'humans in the loop' when automating the deployment of machine learning (ML) models. Why? Because models often perform worse over time. This course covers the human directed safeguards that prevent poorly performing models from deploying into production and the techniques for evaluating models over time. We'll use ModelDB to capture the appropriate metrics that help you identify poorly performing models. We'll review the many factors that affect model performance (i.e., changing users and user preferences, stale data, etc.) and the variables that lose predictive power. We'll explain how to utilize classification and prediction scoring methods such as precision recall, ROC, and jaccard similarity. We'll also show you how ModelDB allows you to track provenance and metrics for model performance and health; how to integrate ModelDB with SparkML; and how to use the ModelDB APIs to store information when training models in Spark ML. Learners should have basic familiarity with the following: Scala or Python; Hadoop, Spark, or Pandas; SBT or Maven; cloud platforms like Amazon Web Services; Bash, Docker, and REST."--Resource description page.

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