This book's GitHub repository, which contains the source code for this chapter, can be found at https://github.com/PacktPublishing/Hands-On-Artificial-Intelligence-on-Amazon-Web-Services.

AI is an umbrella term that describes a branch of computer science that aims to create intelligent agents. The field of AI is highly technical and specialized; there is a broad set of theories, methods, and technologies in AI that allow computers to see (computer vision), to hear (speech recognition), to understand (natural language processing), to speak (text-to-speech), and to think (knowledge reasoning and planning).

It may seem that AI is a buzzword of our current times, but it has existed since the 1950s, when early work on artificial neural networks that mimic led the human brain stirred up excitement for thinking machines. With all the fanfare it receives in the media today, it is hard to believe that this field had to endure two AI winters, where interest in AI research and development dwindled. Today, AI has become popular again, thanks to the increased volume...

To better understand AWS AI offerings, we can group the services into two main groups.

The following diagram shows the subsets of AWS AI capabilities and AWS ML platforms that we will be covering in this book, organized by the two groups:

The first group in the preceding diagram are the AWS AI capabilities. These services are built on top of AWS pre-trained AI technologies. They work right out of the box to provide ready-made intelligence for your applications. You do not need to understand the AI techniques that make them tick, and you do not need to maintain the infrastructure to host...

The AWS CLI is a tool that allows you to interact with AWS services by issuing commands from your Terminal shell. Earlier in this chapter, we interacted with a few AWS services through the web-based AWS Management Console. While the web console is the easiest interface for new users to get familiar with AWS, it can be cumbersome to use during the software development process. With the AWS CLI tool, you get all of the same functionalities as the web console but at your fingertips in the Terminal shell, where most of your development tools reside. This way, your development process is more fluid without the context switching to and from the browser.

The AWS CLI is primarily distributed via the Python package manager; therefore, you need to install Python on your development machine first. Note that we will be using Python for the development of...

Python is one of the most popular programming languages. Thanks to its popularity with the data science and ML community, Python is also one of the fastest-growing programming languages. There is a large number of add-on libraries from its developers and the open source community. These libraries enable Python developers to do almost anything, from data analytics to deep neural networks, from simple scripting to web application development.

For AI and ML, Python is the de facto language. There's the popular scikit-learn library that gives developers access to many useful ML algorithms. There are also many libraries for deep neural networks, such as MXNet and TensorFlow.

We will be using Python for every hands-on projects throughout this book:

• In the first half of this book, we will create intelligent-enabled solutions using AWS AI services...

Now, let's write our first Python application that will detect the objects in the images that are stored in an S3 bucket. To do this, we will leverage boto3 to interact with both the Amazon S3 service and the Amazon Rekognition service:

1. The first source file that we will create is storage_service.py. Create this source file in the ObjectDetectionDemo directory. The following is the Python code for storage_service.py:

import boto3...

In this chapter, we learned that AI has existed for a long time, but it is becoming popular again due to the renewed interested in ML, more specifically, with artificial neural networks. We looked at a few real-world applications of AI and ML. We got an overview of the two groups of AI offerings from AWS; the AWS AI services that give you a quick lift when developing intelligent-enabled applications, and the AWS ML platforms that allow you to build custom AI capabilities that are tailored to your application. We recommended that you try to leverage the provided AWS AI services first, and only develop custom AI capabilities if you have a specialized need and you have a data-competitive advantage.

We got familiar with several services on AWS through hands-on experience, including the AI services and the other complimentary cloud services. We also set up our local development...

You can access the following links for more information on AI on AWS:

• https://www.sciencenews.org/article/future-ai-may-diagnose-eye-problems
• https://www.sas.com/en_us/insights/analytics/what-is-artificial-intelligence.html
• https://adage.com/article/cmo-strategy/google-backed-video-puts-viewers-a-driving-cars/312542/
• https://blog.swiftkey.com/whats-difference-predictive-keyboards-predictive-text/
• http://www.futurile.net/2013/10/03/virtualenv-and-virtualenvwrapper-for-python-development/