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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

Product type Paperback

Published in Aug 2020

Publisher Packt

ISBN-13 9781800209848

Length 732 pages

Edition 1st Edition

Languages

Python

Tools

MongoDB

Concepts

Artificial Intelligence

Authors (6):

Bas Geerdink

Chinmay Arankalle

Kunal Gera

Kevin Liao

Gareth Dwyer

Anand N.S.

+2 more

View More author details

Table of Contents (14) Chapters

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

Summary

In this chapter, we saw examples of prejudice in AI systems through several case studies. We saw how Cambridge Analytica developed an AI based on stolen personal data, how Amazon developed an AI that displayed sexist traits, and how the US justice system, to some extent, relies on AI that displays racist traits.

We built our own AI system that displayed some elements of prejudice and discussed how important it is to be aware of in-built biases, especially when using pre-trained models. We gained experience with the Python library spaCy and saw how word embeddings work. We verified that our sentiment analyzer worked on movie reviews, and then tested it further with some more words associated with prejudices.

In the next chapter, we will be studying the fundamentals of SQL and NoSQL databases by taking a practical approach. We will be learning and performing queries in MySQL, MongoDB, and Cassandra. Don't forget to consider the ethical considerations of any data that...

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Authors (6)

Chinmay Arankalle

Chinmay Arankalle

Chinmay Arankalle has been working with data since day 1 of his career. In his 7 years in the field, he has designed and built production-grade data systems for telecommunication, pharmaceutical, and life science domains, where new and exciting challenges are always on the horizon. Chinmay started as a software engineer and, over time, has worked extensively on data cleaning, pre-processing, text mining, transforming, and modeling. Production-ready big data systems are his forte.

See other products by Chinmay Arankalle

Anand N.S.

Anand N.S.

Anand N.S. has more than two decades of technology experience working, with a strong hands-on track record of application of artificial intelligence, machine learning, and data science to create measurable business outcomes. He has been granted several US patents in the areas of data science, machine learning, and artificial Intelligence. Anand has a B.Tech in Electrical Engineering from IIT Madras and an MBA with a Gold Medal from IIM Kozhikode.

See other products by Anand N.S.

Kevin Liao

Kevin Liao

Kevin Liao has a rich experience in applying data science in different industries by building classes of data science solutions for applications ranging from startup fintech products to web-scale consumer-facing web/mobile pages. Kevin started his career as a statistician/quant in a fintech startup. As data scaled, he honed his data engineering skills and established best practices for web-scale data science solutions. Even after moving to a consumer-facing product company, Kevin has continued to develop data science experiences in an online environment space, which requires extremely low latency solutions.

See other products by Kevin Liao

Kunal Gera

Kunal Gera

Kunal Gera has been involved in unleashing solutions with the help of data. He has successfully implemented various projects in the field of predictive analytic and data analysis using the analytical skill gained over his professional experience and education.

See other products by Kunal Gera

Bas Geerdink

Bas Geerdink

Bas Geerdink is a programmer, scientist, and IT manager. He works as a technology lead in the AI and big data domain. Having an academic background in artificial intelligence and informatics, he has his research on reference architectures for big data solutions published at the IEEE conference ICITST 2013. Bas has a background in software development, design, and architecture with a broad technical view from C++ to Prolog to Scala. He occasionally teaches programming courses and is a regular speaker at conferences and informal meetings, where he presents a mix of market context, his own vision, business cases, architecture, and source code in an interesting way.

See other products by Bas Geerdink

Gareth Dwyer

Gareth Dwyer

Gareth Dwyer hails from South Africa but now lives in Europe. He is a software engineer and author and is currently serving as the CTO at the largest coding education provider in Africa. Gareth is passionate about technology, education, and sharing knowledge through mentorship. He holds four university degrees in computer science and machine learning, with a specialization in natural language processing. He has worked with companies such as Amazon Web Services and has published many online tutorials as well as the book Flask by Example.

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