Using Apache Spark for AI Development

Apache Spark    |    Intermediate
  • 13 videos | 36m 52s
  • Includes Assessment
  • Earns a Badge
Rating 4.2 of 20 users Rating 4.2 of 20 users (20)
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

  • Discover the key concepts covered in this course
    Identify cases in which it is advantageous to use spark over other platforms
    Define a resilient distributed dataset and identify typical sources of data
    Specify the unique features of a resilient distributed dataset
    Describe how to create a resilient distributed dataset
    List possible operations with resilient distributed datasets and define their roles
    List potential sources of data for a spark dataframe and outline how to import these into spark
  • Name the features of a spark dataframe and some useful operations with which to use it
    Outline how to create a spark dataframe
    Specify how spark ml pipelines can be used for creating and tuning ml models
    Describe fundamental concepts of spark ml pipelines
    Create an ml pipeline using spark ml pipelines
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 2m 46s
  • 5m
    In this video, you will identify cases in which it is advantageous to use Spark over other platforms. FREE ACCESS
  • Locked
    3.  Resilient Distributed Dataset Sources
    3m 22s
    Learn how to define a resilient distributed dataset and identify typical sources of data. FREE ACCESS
  • Locked
    4.  Resilient Distributed Dataset Features
    2m 2s
    Upon completion of this video, you will be able to specify the unique features of a resilient distributed dataset. FREE ACCESS
  • Locked
    5.  Resilient Distributed Dataset Creation
    2m 43s
    After completing this video, you will be able to describe how to create a resilient distributed dataset. FREE ACCESS
  • Locked
    6.  Resilient Distributed Dataset Operations
    2m 53s
    After completing this video, you will be able to list possible operations with resilient distributed datasets and define their roles. FREE ACCESS
  • Locked
    7.  Spark DataFrame Sources
    1m 58s
    After completing this video, you will be able to list potential sources of data for a Spark DataFrame and outline how to import these into Spark. FREE ACCESS
  • Locked
    8.  Spark DataFrame Features
    1m 42s
    Upon completion of this video, you will be able to name the features of a Spark DataFrame and some useful operations to use with it. FREE ACCESS
  • Locked
    9.  Spark DataFrame Creation
    2m 46s
    In this video, you will learn how to create a Spark DataFrame. FREE ACCESS
  • Locked
    10.  Spark ML Pipelines
    3m 55s
    Upon completion of this video, you will be able to specify how Spark ML Pipelines can be used for creating and tuning machine learning models. FREE ACCESS
  • Locked
    11.  Spark ML Pipeline Concepts
    2m
    Upon completion of this video, you will be able to describe fundamental concepts of Spark ML pipelines. FREE ACCESS
  • Locked
    12.  Creating a Pipeline with Spark ML
    4m 55s
    In this video, you will create an ML pipeline using Spark ML pipelines. FREE ACCESS
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    13.  Course Summary
    51s
    In this video, we will summarize the key concepts covered in this course. FREE ACCESS

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