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Deploying Spark ML pipelines in production on AWS : how to publish pipeline artifacts and run pipelines in production /

"Translating a Spark application from running in a local environment to running on a production cluster in the cloud requires several critical steps, including publishing artifacts, installing dependencies, and defining the steps in a pipeline. This video is a hands-on guide through the process...

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Bibliographic Details
Call Number:Libro Electrónico
Format: Electronic Video
Language:Inglés
Published: [Place of publication not identified] : O'Reilly, 2017.
Subjects:
Online Access:Texto completo (Requiere registro previo con correo institucional)
Description
Summary:"Translating a Spark application from running in a local environment to running on a production cluster in the cloud requires several critical steps, including publishing artifacts, installing dependencies, and defining the steps in a pipeline. This video is a hands-on guide through the process of deploying your Spark ML pipelines in production. You'll learn how to create a pipeline that supports model reproducibility--making your machine learning models more reliable--and how to update your pipeline incrementally as the underlying data change. Learners should have basic familiarity with the following: Scala or Python; Hadoop, Spark, or Pandas; SBT or Maven; Amazon Web Services such as S3, EMR, and EC2; Bash, Docker, and REST."--Resource description page
Item Description:Title from title screen (Safari, viewed January 15, 2018).
Release date from resource description page (Safari, viewed January 15, 2018).
Physical Description:1 online resource (1 streaming video file (23 min., 20 sec.))