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Deploying Spark ML Pipelines in Production on AWS

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发表于 2017-12-20 08:59:13 | 显示全部楼层 |阅读模式

Deploying Spark ML Pipelines in Production on AWS
MP4 | Video: AVC 1920x1080 | Audio: AAC 48KHz 2ch | Duration: 23M | 818 MB
Genre: eLearning | Language: English
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.
Understand how various cloud ecosystem components interact (i.e., Amazon S3, EMR, EC2, and so on)
Learn how to architect the components of a cloud ecosystem into an end-to-end model pipeline
Explore the capabilities and limitations of Spark in building an end-to-end model pipeline
Learn to write, publish, deploy, and schedule an ETL process using Spark on AWS using EMR
Understand how to create a pipeline that supports model reproducibility and reliability

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永久VIP

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发表于 2017-12-20 11:47:37 | 显示全部楼层
牛牛 666。
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发表于 2017-12-24 14:23:08 | 显示全部楼层
牛牛 666。
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永久VIP

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发表于 2017-12-27 16:27:43 来自手机 | 显示全部楼层
东西很值得!东西很多
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发表于 2018-1-13 21:52:38 | 显示全部楼层
东西很值得!东西很多
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永久VIP

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发表于 2018-4-5 07:52:52 | 显示全部楼层
强烈支持楼主  东西很值得
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