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 had a hands-on overview of SQL and NoSQL databases. We looked into their usage, their best practices, and even various strategies to achieve near-perfect latency.

We learned about how to create a table, insert the data into a table, and how to select data from a table in various databases. Furthermore, we have explored complex aggregation over data, along with the resulting visualizations through sample data analytics.

Understanding the different types of databases and their evolutions, along with the various types of query languages and data modeling from this chapter, will help you to get to grips with the essence of big data file formats in the coming chapters. To start with big data, you will need to learn about the use of file formats. Such concepts can be assimilated by understanding the nitty-gritty of databases.

In the next chapter, you will learn about big data file formatting, along with compression, partitioning, and read-write strategy...