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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
Author Profile Icon Bas Geerdink
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

1. Data Storage Fundamentals

Overview

In this chapter, we will explore the broad range of capabilities of AI and look at some of the fields that it is changing. We will cover four areas in which AI is used in detail: medicine, language translation, subtitle generation, and forecasting. Then we will dive into a text classification example where you will build your first AI system – a basic text classifier that can identify when a news headline is regarded as "clickbait." We will look at optimization – an important topic for most machine learning systems that need to operate on a large scale. Finally, we will examine different kinds of hardware, including memory, processes, and storage, and will also see how we can reduce costs when renting this hardware from a cloud vendor.

By the end of this chapter, you will understand what kind of tasks machine learning can be used to perform. You will be able to build your own basic machine learning systems, using a popular Python library, sklearn. You will also be able to optimize the hardware of large systems and reduce costs while storing your data in a logical way.

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