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)
4. The Ethics of AI Data Storage

Introduction to File Formats

Now, let's understand the file structure in detail and distinguish between these file formats. This section will decompose the file formats and dive into the structure of files to elaborate on the efficiency of each file format.


Apache Parquet is an open-source column-oriented representation and stores data in an optimized columnar format. It is language-independent and framework-independent because the objective of creating this format was to optimize the operation and storage of data across Hadoop.

Shortly after its introduction, it acquired popularity in the industry. The reasons for its acceptance are primarily the fast retrieval and processing capabilities that it offers. However, writes are usually time-consuming and considerably expensive.

As it is a columnar-based format, homogenous data is stored together, resulting in better compression. The compression and encoding scheme can have a significant impact on performance.