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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Detalles Bibliográficos
Clasificación:Libro Electrónico
Formato: Electrónico Video
Idioma:Inglés
Publicado: [Place of publication not identified] : O'Reilly, 2017.
Temas:
Amazon Web Services (Firm)
SPARK (Electronic resource)
Machine learning.
Cloud computing.
Apprentissage automatique.
Infonuagique.
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)

MARC

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520 |a "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 
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