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

Cassandra

The topic of NoSQL isn't complete without discussing columnar storage. In Cassandra, the data is stored and read in columns instead of rows. Each column is stored separately with the same row offset related to the table. We will study offset in the Data Modeling subsection:

Figure 5.50: Keyspace and column family internal structure

Cassandra is a mixture of structured data, consistency, high scalability, and no single point of failure, and also has a powerful column family design. This particular database was developed by Facebook in 2008 and is horizontally scalable. Imagine you run a multinational company. You have thousands of employees, but you want the information of all the employees at the London location. In such a scenario, a traditional relational database will scan each row, parse it, and compare the u_location column value against the query. This is extremely time-consuming.

On the other hand, the columnar store only requires columns...