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

NoSQL Databases

In the previous section, we looked at how SQL databases are used in the industry, along with some examples. However, with all the great things SQL databases can do, there is still significant room for improvement when it comes to dealing with complex and huge data with quick retrieval requirements. The times before and after NoSQL databases were created can be seen in the following diagram:

Figure 5.34: NoSQL database formats

These NoSQL databases evolved because of the fundamental limitations of SQL databases. The data in almost every sector has grown exponentially over time and the variety of usage has also expanded. Earlier, databases were limited to enterprise resource systems, research projects, telecommunications, and so on, but today, new domains have emerged with huge data in a variety of formats, such as social media and profiling millions of users, oil and gas, with billions of binary records, e-commerce, and life sciences. Because...