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Mastering Apache Spark 2.x

You're reading from   Mastering Apache Spark 2.x Advanced techniques in complex Big Data processing, streaming analytics and machine learning

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781786462749
Length 354 pages
Edition 2nd Edition
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Author (1):
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Romeo Kienzler Romeo Kienzler
Author Profile Icon Romeo Kienzler
Romeo Kienzler
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Table of Contents (15) Chapters Close

Preface 1. A First Taste and What’s New in Apache Spark V2 FREE CHAPTER 2. Apache Spark SQL 3. The Catalyst Optimizer 4. Project Tungsten 5. Apache Spark Streaming 6. Structured Streaming 7. Apache Spark MLlib 8. Apache SparkML 9. Apache SystemML 10. Deep Learning on Apache Spark with DeepLearning4j and H2O 11. Apache Spark GraphX 12. Apache Spark GraphFrames 13. Apache Spark with Jupyter Notebooks on IBM DataScience Experience 14. Apache Spark on Kubernetes

What does the new API look like?


When it comes to machine learning on Apache Spark, we are used to transforming data into an appropriate format and data types before we actually feed them to our algorithms. Machine learning practitioners around the globe discovered that the preprocessing tasks on a machine learning project usually follow the same pattern:

  • Data preparation
  • Training
  • Evaluating
  • Hyperparameter tuning

Therefore, the new ApacheSparkML API supports this process out of the box. It is called pipelines and is inspired by scikit-learn http://scikit-learn.org, a very popular machine learning library for the Python programming language. The central data structure is a DataFrame and all operations run on top of it.

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