Knowing what AI is only gets you halfway. The other half is knowing what to use it for, and just as importantly, when not to use it. Exam questions and real projects both start from a business problem, not from a technology, so the skill is matching the problem to the right kind of solution. This post surveys the most common AI use cases, links each to the AWS services you are likely to meet, and then looks at the situations where AI is the wrong choice. 1. Start with the problem, not the technology A useful habit is to describe a problem in plain words before naming any technology: “we need to sort incoming support emails by topic”, “we want to read data from scanned invoices”, “we need to predict which customers will cancel”. Once the problem is clear, you can ask which category of AI fits it. The categories below are the ones you will see most often. 2. Common use cases by category Category What the AI does Example AWS service you may meet Computer vision Analyses ...
People use “AI”, “machine learning”, “deep learning”, “foundation model” and “generative AI” as if they meant the same thing. They do not, and the AWS exams expect you to tell them apart. This post sets out the vocabulary you will use for the rest of this wiki: what each term means, how the terms nest inside one another, and which AWS service you are most likely to meet for each. 1. The big picture: nested ideas The easiest way to remember the relationship is as a set of boxes, each one sitting inside a wider one. Artificial intelligence (AI) – the broad goal: machines doing tasks that normally need human intelligence Machine learning (ML) – AI that learns patterns from data instead of following hand-written rules Deep learning (DL) – ML that uses neural networks with many layers Generative AI – deep learning models that create new content such as text, images, audio and code Two points about this picture are worth remembering. First, foundation models are the large, g...