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

Summary

In this chapter, we have discussed many ways to prepare data for machine learning and other forms of AI. Raw data from source systems had to be transported across the data layers of a modern data lake, including a historical data archive, a set of (virtualized) analytics datasets, and a machine learning environment. There are several tools for creating such a data pipeline: simple scripts and traditional software, ETL tools, big data processing frameworks, and streaming data engines.

We have also introduced the concept of feature engineering. This is an important piece of work in any AI system, where data is prepared to be consumed by a machine learning model. Independent of the programming language and frameworks that are chosen for this, an AI team has to spend significant time writing the features and ensuring that the resulting code and binaries are well managed and deployed, together with the models themselves.

We have performed exercises and activities where we...