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

Data Layers

An AI system consists of multiple data storage layers that are connected with Extract, Transform, and Load (ETL) or Extract, Load, and Transform (ELT) pipelines. Each separate storage solution has its own requirements, depending on the type of data that is stored and the usage pattern. The following figure shows this concept:

Figure 2.3: Conceptual overview of the data layers in a typical AI solution

From a high-level viewpoint, the backend (and thus, the storage systems) of an AI solution is split up into three parts or layers:

  • Raw data layer: Contains copies of files from source systems. Also known as the staging area.
  • Historical data layer: The core of a data-driven system, containing an overview of data from multiple source systems that have been gathered over time. By stacking the data rather than replacing or updating old values, history is preserved and time travel (being able to make queries over a data state in the past) is...