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210123 ||| eng |
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4 |
|a Q325.5
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| 100 |
1 |
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|a Slepicka, Jason
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| 245 |
0 |
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|a Deploying Spark ML pipelines in production on AWS
|b how to publish pipeline artifacts and run pipelines in production
|c with Jason Slepicka
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| 260 |
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|a [Place of publication not identified]
|b O'Reilly
|c 2017
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| 300 |
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|a 1 streaming video file (23 min., 20 sec.)
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| 653 |
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|a SPARK (Electronic resource) / http://id.loc.gov/authorities/names/n2004007265
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| 653 |
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|a Cloud computing / fast
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| 653 |
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|a Machine learning / http://id.loc.gov/authorities/subjects/sh85079324
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| 653 |
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|a Infonuagique
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| 653 |
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|a Amazon Web Services (Firm) / http://id.loc.gov/authorities/names/no2015140713
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| 653 |
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|a SPARK (Electronic resource) / fast
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| 653 |
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|a Cloud computing / http://id.loc.gov/authorities/subjects/sh2008004883
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| 653 |
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|a Machine learning / fast
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| 653 |
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|a Apprentissage automatique
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| 653 |
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|a Amazon Web Services (Firm) / fast
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b OREILLY
|a O'Reilly
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| 500 |
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|a Title from title screen (Safari, viewed January 15, 2018). - Release date from resource description page (Safari, viewed January 15, 2018)
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|u https://learning.oreilly.com/videos/~/9781491988879/?ar
|x Verlag
|3 Volltext
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| 082 |
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|a 000
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| 520 |
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|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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