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

Introduction

In the previous chapter, we covered the fundamentals of data storage. In this chapter, we'll dive a little deeper into the architecture of Artificial Intelligence (AI) solutions, starting with the requirements that define them. This chapter will be a mixture of theoretical content and hands-on exercises, with real-life examples where AI is actively used.

Let's say you are a solution architect involved in the design of a new data lake. There are a lot of technology choices to be made that would have an impact on the people involved and on the long-term operations of the organization. It is great to have a set of requirements at the start of the project that each decision could be based on. Storing data essentially means writing data to disk or memory so that it is safe, secure, findable, and retrievable. There are many ways to store data: on-premise, in the cloud, on disk, in a database, in memory, and so on. Each way fulfills a set of requirements to a greater...