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

Automating a Data Pipeline

You may think that multi-stage jobs are complicated. Users are required to run multiple commands in a specific sequence to complete tasks. One of the principles of workflow management is the minimization of human interaction. Human interaction is usually error-prone. If someone runs commands in the wrong order, there will be different results. We want to remove this manual process, which means we need to automate this job.

Bash is a Unix shell. It's a command language that can be used directly at the command line. Often, people use Bash as glue code to stitch different software systems or tools together, as well as using it for the automation of jobs.

In the next exercise, we will leverage Bash to automate the multi-stage data pipeline of Exercise 9.03, Creating a Multi-Stage Data Pipeline.

Exercise 9.04: Automating a Multi-Stage Data Pipeline Using a Bash Script

In the last exercise, we created four Python scripts, one for each stage of...