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


Machine learning, which is a subset of Artificial Intelligence (AI), has had a major influence on nearly every field you can imagine and can solve a wide variety of problems and tasks. Do you want to detect cancer better? You can train an image classifier to inspect mammograms. Do you want to communicate with people in other languages? Machine translation will help you. From ambitious projects such as self-driving cars and astronomical discoveries to fixing minor annoyances such as email spam, machine learning has taken the world by storm, and those who understand what it can do and how to build machine learning systems will be at the forefront of human advancement.

At the heart of any machine learning project is data. Many people, on first coming across the concept of machine learning, assume that it is possible to take mounds of data, shove it into a machine, and have the machine autonomously learn. But it's not so simple. Instead, machines need meticulously structured, organized, and clean data, often in huge quantities. The more data there is, the more difficult it becomes to store, process, and analyze the data, and it is therefore vital to optimize data storage at all stages of storage and usage.

This is a problem. How can we build efficient machine learning systems that do not waste our time or resources?

This course will show you practical real-world examples of how to do exactly that. You will learn how to make your data work for you as efficiently as possible, often by example.

We assume that you are no novice at working with data and that you understand and have used various filesystems, file formats, databases, and storage solutions for digital data. In this book, we will focus specifically on data for machine learning and show how this differs from storing general-purpose data.

Machine learning comes in many different forms, but concepts from linear algebra are core to many of the most important machine learning algorithms. In classical computer science, the focus is often on data structures such as arrays, linked lists, hash tables, and trees. In machine learning, while these structures are still important, you will more often need to work with data in the form of vectors, matrices, and tensors.

Because of this focus on data structures from linear algebra, other components of storage solutions have nuances too. Some processors are optimized at the hardware level for vectorized operations. Some file formats handle this kind of data better too, and there are specialized data structures to store data in this form efficiently as well.