Summary

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 environment for AI applications, including Python, the AWS CLI, and Python virtual environments. We then created our first intelligent-enabled application using the Amazon S3 and Amazon Rekognition services.

In the next chapter, we will dive into the components and the architecture of AI applications. We will set up an architecture template for many of the upcoming hands-on projects, and more importantly, we will cover the design principles and decisions that this architecture template is based on.