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

Workflow Management with Airflow

So far, we have learned how to create data pipelines and different types of workflows, including linear and non-linear ones. We define and implement workflows in Python scripts and use Bash to automate workflows. However, that is not enough for us to be able to manage workflows on a large scale. We are going to take workflow management to the next level by solving the following problems:

  • Can we find a standardized way to define workflow dependency instead of writing a customized Bash script?
  • Can we define data operations with a consistent interface instead of writing a Python program with a customized CLI?
  • Can we have a standardized way to log the pipeline's running status?
  • Can we monitor a running workflow? Can we schedule workflows?

The answer to all of these problems is Airflow. Airflow is a horizontally scalable, distributed workflow management system that allows us to specify complex workflows using Python code...