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

Common Input Files

Let's learn about the types of input files that are commonly used to exchange data between systems and the ways to convert them into various big data file formats. This section will also provide you with the programming skills required to transform these input files for the big data environment.

CSV – Comma-Separated Values

A CSV is a text file used to store tabular data separated by a comma. CSV is row-based data storage where each row is separated by a new line. For the exchange of tabular data, CSV files are frequently used.

The first row or header row of CSV files contains the schema detail, that is, column names for the data but not the type of data. CSV files fail to represent relational data, which means that a common column in multiple files does not have any relationship or hierarchy. Foreign keys are stored in columns of one or more files, but the CSV format itself does not express the linkage between these files.

The following figure...