Book Image

The Artificial Intelligence Infrastructure Workshop

By : Chinmay Arankalle, Gareth Dwyer, Bas Geerdink, Kunal Gera, Kevin Liao, Anand N.S.
Book Image

The Artificial Intelligence Infrastructure Workshop

By: Chinmay Arankalle, Gareth Dwyer, Bas Geerdink, Kunal Gera, Kevin Liao, Anand N.S.

Overview of this book

Social networking sites see an average of 350 million uploads daily - a quantity impossible for humans to scan and analyze. Only AI can do this job at the required speed, and to leverage an AI application at its full potential, you need an efficient and scalable data storage pipeline. The Artificial Intelligence Infrastructure Workshop will teach you how to build and manage one. The Artificial Intelligence Infrastructure Workshop begins taking you through some real-world applications of AI. You’ll explore the layers of a data lake and get to grips with security, scalability, and maintainability. With the help of hands-on exercises, you’ll learn how to define the requirements for AI applications in your organization. This AI book will show you how to select a database for your system and run common queries on databases such as MySQL, MongoDB, and Cassandra. You’ll also design your own AI trading system to get a feel of the pipeline-based architecture. As you learn to implement a deep Q-learning algorithm to play the CartPole game, you’ll gain hands-on experience with PyTorch. Finally, you’ll explore ways to run machine learning models in production as part of an AI application. By the end of the book, you’ll have learned how to build and deploy your own AI software at scale, using various tools, API frameworks, and serialization methods.
Table of Contents (14 chapters)
Preface
4
4. The Ethics of AI Data Storage

Summary

In this chapter, we have learned about common input file formats for big data, such as CSV and JSON. We also learned about popular file formats, namely Parquet, Avro, and ORC, which are useful in the big data environment and looked at essential decision points for making a choice on which to use. We explored the conversion to each of these file formats from the CSV and JSON formats and executed them in a big data environment using Spark and Scala. To strengthen the concept, we executed each format conversion in the respective exercises.

At the end of the chapter, we looked at a real-world business problem and concluded which was the most suitable file format based on the selection criteria learned in this chapter.

In the next chapter, we will extensively cover the vital infrastructure of the big data environment known as Spark. This will lay a strong foundation of the concept and also lead us through the journey of creating our first pipeline in Spark.