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 covered many concepts of workflow management and job control. We started by creating a simple data workflow with a single Python script. We then added more steps into the workflow and broke the workflow down into a multi-stage workflow. Next, we used Bash to compose as well as automate workflows. Lastly, we studied DAGs and implemented them using the open-source tool Airflow.

With the concepts and techniques that you have learned in this chapter, you will be able to tackle more sophisticated problems in the areas of AI and data science. Moreover, you will continue to learn and build experience on top of what you have gained from this chapter.

In the next chapter, you will learn about data solutions from public cloud providers such as Amazon Web Services. The concepts of implementing data operations and creating a data pipeline will be our building blocks for the next chapter. We will continue to build more sophisticated data storage solutions for use in AI...