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 this chapter, we discussed the non-functional requirements for data storage solutions. It has become clear that a data lake, which is an evolution of a data warehouse, consists of multiple layers that have their own requirements and thus technology. We have discussed the key requirements for a raw data store where primarily flat files need to be stored in a robust way, for a historical database where temporal information is saved, and for analytics data stores where fast querying is necessary. Furthermore, we have explained the requirements for a streaming data engine and for a model development environment. In all cases, requirements management is an ongoing process in an AI project. Rather than setting all the requirements in stone at the start of the project, architects and developers should be agile, revisiting and revising the requirements after every iteration.

In the next chapter, we will connect the layers of the architecture we have explored in this chapter by...