Using Apache Spark for AI Development
Apache Spark 2.4
| Intermediate
- 13 Videos | 36m 52s
- Includes Assessment
- Earns a Badge
Spark is a leading open-source cluster-computing framework that is used for distributed databases and machine learning. Although not primarily designed for AI, Spark allows you to take advantage of data parallelism and the large distributed systems used in AI development. AI practitioners should recognize when to use Spark for a particular application. In this course, you'll explore advanced techniques for working with Apache Spark and identify the key advantages of using Spark over other platforms. You'll define the meaning of resilient distributed databases (RDDs) and explore several workflows related to them. You'll move on to recognize how to work with a Spark DataFrame, identifying its features and use cases. Finally, you'll learn how to create a machine learning pipeline using Spark ML Pipelines.
WHAT YOU WILL LEARN
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discover the key concepts covered in this courseidentify cases in which it is advantageous to use Spark over other platformsdefine a resilient distributed dataset and identify typical sources of dataspecify the unique features of a resilient distributed datasetdescribe how to create a resilient distributed datasetlist possible operations with resilient distributed datasets and define their roleslist potential sources of data for a Spark DataFrame and outline how to import these into Spark
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name the features of a Spark DataFrame and some useful operations with which to use itoutline how to create a Spark DataFramespecify how Spark ML Pipelines can be used for creating and tuning ML modelsdescribe fundamental concepts of Spark ML pipelinescreate an ML pipeline using Spark ML pipelinessummarize the key concepts covered in this course
IN THIS COURSE
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1.Course Overview2m 46sUP NEXT
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2.SPARK vs. Other Platforms5m
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3.Resilient Distributed Dataset Sources3m 22s
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4.Resilient Distributed Dataset Features2m 2s
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5.Resilient Distributed Dataset Creation2m 43s
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6.Resilient Distributed Dataset Operations2m 53s
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7.Spark DataFrame Sources1m 58s
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8.Spark DataFrame Features1m 42s
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9.Spark DataFrame Creation2m 46s
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10.Spark ML Pipelines3m 55s
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11.Spark ML Pipeline Concepts2m
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12.Creating a Pipeline with Spark ML4m 55s
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13.Course Summary51s
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