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

Introduction

In the previous chapter, we discussed the layers of a data-driven system and explained the important storage requirements for each layer. The storage containers in the data layers of AI solutions serve one main purpose: to build and train models that can run in a production environment. In this chapter, we will discuss how to transfer data between the layers in a pipeline so that the data is prepared to be used to train a model to create an actual forecast (called the execution or scoring of the model).

In an Artificial Intelligence (AI) system, data is continuously updated. Once data enters the system via an upload, application program interface (API), or data stream, it has to be stored securely and typically goes through a few ETL steps. In systems that handle streaming data, the incoming data has to be directed into a stable and usable data pipeline. Data transformations have to be managed, scheduled, and orchestrated. Further, the lineage of the data has to...