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Deploying Spark ML Pipelines in Production on AWS

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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Tags: Deploying, Pipelines, Production

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