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


In previous chapters, we learned about various hardware and software infrastructures for AI practices. We learned about different databases and data solutions as well as their use cases. We learned about big data computing engines such as Spark, which allows engineers to process web-scale data. We also learned about using workflow management systems such as Airflow to manage data pipelines at scale. We also learned a lot about cloud data solutions and how to leverage cloud data storage and perform basic data-related tasks.

This chapter will focus on the science and the mathematical side of artificial intelligence. Without a proper understanding of the theory that underpins AI, we simply cannot build a robust AI application. If we can understand the math and science behind AI, then we will be able to apply different algorithms to solve different real-world problems. With the skills you will gain from this chapter, you will be able to innovate new AI algorithms to solve...