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

ETL

ETL is the standard term that is used for Extracting, Transforming, and Loading data. In traditional data warehousing systems, the entire data pipeline consists of multiple ETL steps that follow after each other to bring the data from the source to the target (usually a report on a dashboard). Let's explore this in more detail:

E: Data is extracted from a source. This can be a file, a database, or a direct call to an API or web service. Once loaded with a query, the data is kept in memory, ready to be transformed. For example, a daily export file from a source system that produces client orders is read every day at 01:00.

T: The data that was captured in memory during the extraction phase (or in the loading phase with ELT) is transformed using calculations, aggregations, and/or filters into a target dataset. For example, the customer order data is cleaned, enriched, and narrowed down per region.

L: The data that was transformed is loaded (stored) into a data store...