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

1. Data Storage Fundamentals

Overview

In this chapter, we will explore the broad range of capabilities of AI and look at some of the fields that it is changing. We will cover four areas in which AI is used in detail: medicine, language translation, subtitle generation, and forecasting. Then we will dive into a text classification example where you will build your first AI system – a basic text classifier that can identify when a news headline is regarded as "clickbait." We will look at optimization – an important topic for most machine learning systems that need to operate on a large scale. Finally, we will examine different kinds of hardware, including memory, processes, and storage, and will also see how we can reduce costs when renting this hardware from a cloud vendor.

By the end of this chapter, you will understand what kind of tasks machine learning can be used to perform. You will be able to build your own basic machine learning systems, using a popular Python library, sklearn. You will also be able to optimize the hardware of large systems and reduce costs while storing your data in a logical way.