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

Historical Data

The historical data layer contains data stores that hold all data from a certain point in the past (for example, the start of the company) up until now. In most cases, this data is considered to be important to run a business, and in some cases, even vital for its existence. For example, the historical data layer of a newspaper agency contains sources, reference material, interviews, media footage, and so on, all of which were used to publish news articles. Data is stored in blobs, file shares, and relational tables, often with primary and foreign keys (enforced by the infrastructure or in software). The data can be modeled to a standard such as a data vault to preserve historical information. This data layer is responsible for keeping the truth, which means it is highly regulated and governed. Any data that is inserted into one of the tables in this layer has gone through several checks, and metadata is stored next to the actual data to keep track of the history and...