Introduction
What this course teaches
The story: Think of a hardware store owner deciding whether to buy a new power tool. They need to know what jobs it's used for, when a simple hand tool would do the job better, which model to pick, what could go wrong with it, and how to tell after a month whether it was worth the money.
In AI/AWS terms: This course is that buying guide for AI. After it you should be able to:
- Recognize real-world AI applications and the business needs they address.
- Tell when AI and ML are not the right solution.
- Match use cases to supervised, unsupervised, and reinforcement learning.
- Name the capabilities and challenges of generative AI.
- Choose a generative AI model using the right selection factors.
- Pick business metrics that show whether a generative AI application is working.
For the exam: Knowing when AI is not the answer is as much a part of this domain as knowing when it is.
Where AI shows up
The story: Electricity isn't an industry of its own. It's in the factory, the hospital, the school, the shop, the lab, the train, and the TV studio. AI is heading the same way.
In AI/AWS terms: AI helps in manufacturing, healthcare, education, retail, life sciences, transportation, and media. Common uses are summarization, code generation, content creation, chatbots, virtual assistants, anomaly detection, and contact center analytics.
For the exam: AI is used across almost every industry. Recognize the common uses: summarization, code generation, content creation, chatbots, anomaly detection, contact center analytics.