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The Artificial Intelligence Infrastructure Workshop

You're reading from   The Artificial Intelligence Infrastructure Workshop Build your own highly scalable and robust data storage systems that can support a variety of cutting-edge AI applications

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Product type Paperback
Published in Aug 2020
Publisher Packt
ISBN-13 9781800209848
Length 732 pages
Edition 1st Edition
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Authors (6):
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Bas Geerdink Bas Geerdink
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Bas Geerdink
Chinmay Arankalle Chinmay Arankalle
Author Profile Icon Chinmay Arankalle
Chinmay Arankalle
Kunal Gera Kunal Gera
Author Profile Icon Kunal Gera
Kunal Gera
Kevin Liao Kevin Liao
Author Profile Icon Kevin Liao
Kevin Liao
Gareth Dwyer Gareth Dwyer
Author Profile Icon Gareth Dwyer
Gareth Dwyer
Anand N.S. Anand N.S.
Author Profile Icon Anand N.S.
Anand N.S.
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Table of Contents (14) Chapters Close

Preface
1. Data Storage Fundamentals 2. Artificial Intelligence Storage Requirements FREE CHAPTER 3. Data Preparation 4. The Ethics of AI Data Storage 5. Data Stores: SQL and NoSQL Databases 6. Big Data File Formats 7. Introduction to Analytics Engine (Spark) for Big Data 8. Data System Design Examples 9. Workflow Management for AI 10. Introduction to Data Storage on Cloud Services (AWS) 11. Building an Artificial Intelligence Algorithm 12. Productionizing Your AI Applications Appendix

Understanding Various Spark Actions

Spark actions trigger specified transformations. Transformations create RDDs from another RDD. Actions are the operations that are performed on RDDs to give non-RDD values.

Popular actions include reduce, collect, count, first, and s. Actions are executed and values of actions are stored back in Spark drivers or external storage systems.

Let's understand transformations in more detail:

  • reduce(func): This aggregates the elements of a dataset by executing a function on them. reduce works only with commutative and associative functions as it runs in parallel. For example, reduce could be taking (a, b) as the two inputs and having a+b as one output. Say if the input data is {1,2,…100}, using the sum function on reduce would result in {5050}, which is the sum of all the elements of the dataset.
  • collect(): This returns all the elements in a dataset. This is the equivalent of select * in SQL. For example, if the dataset...
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