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


What makes Spark one of the most popular analytics engines? How did Spark evolve to become the parallel processing engine of choice? This chapter will help you get answers to these questions and more.

In the previous chapter, we learned about the various big data file formats, including Parquet, AVRO, and ORC, and how to use them. In this chapter, we will solve the challenge of processing large volumes of data that is dynamic, real-time, and grows exponentially in a short period of time. We will learn about systems that can read, write, and process data exponentially faster than sequential processing. This is facilitated by having parallel processing in clusters, which was the origin of Hadoop and MapReduce. Companies including eBay, Facebook, Twitter, and Google used Hadoop and MapReduce extensively. Later, they found that these systems were not designed for iterative machine learning paradigms. Machine learning models require several iterations to fine-tune the hyperparameters...