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

Analytics Data

The responsibility of the analytics layer of an AI system is to make data fast and available for machine learning models, queries, and so on. This can be achieved by caching data efficiently or by virtualizing views and queries where needed to materialize these views.

Performance

The data needs to be quickly available for ad hoc queries, reports, machine learning models, and so on. Therefore, the data schema that is chosen should reflect a "schema-on-read" pattern rather than a "schema-on-write" one. When caching data, it can be very efficient to store the data in a columnar NoSQL database for fast access. This would mean the duplication of data in many cases, but that's all right since the analytics layer is not responsible for maintaining "one version of the truth." We call these caches data marts. They are usually specific for one goal, for example, retrieving the sales data of the last month.

In modern data lakes, the entire...