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

Summary

This chapter provided you with an understanding of Apache Spark. We began with the context of what problems Hadoop and MapReduce resolved, and the gaps that remain. Spark addresses the issue of iterative processing for machine learning algorithms and supports real-time querying and processing of streaming data. We introduced the concept of RDD, which is the core construct of Spark. We also learned how to use the Databricks platform and launch clusters and notebooks in it. We then moved to understanding transformations and actions, which form the key execution steps. Using a combination of transformations and actions, it is possible to create a pipeline. We covered several examples of transformations and actions and how to use them. We learned about transformations, including map, filter, union, and intersection, and also learned how to use actions such as count, collect, reduce, first, and take. We then touched on some of the best practices to keep in mind when using Spark...