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

This chapter gave you an overview of the theory underpinning machine learning algorithms, looking at constructing a loss function, and using gradient descent. These fundamental concepts will help you better understand a lot of the implementation details of current deep learning practices. They will also help you separate yourself from your peers. The exercises in this chapter focused on hands-on practical skills such as building and training machine learning algorithms for AI from scratch. With the practical skills you learned from this chapter, you will be able to build machine learning models to solve real-world problems.

We started by training a simple linear regression model and implementing a gradient descent algorithm using NumPy from scratch, which helped us better understand how to train a machine learning model. Then we moved on to building training models with PyTorch low-level modules. We also talk about batch gradient descent versus mini-batch SGD in depth. We...