AI workload types on Azure

Teach

Azure AI Fundamentals starts with choosing the right workload type before choosing services or models.

Core AI workload types

• Regression predicts a continuous value (for example, delivery time or sales amount).
• Classification predicts a category (for example, spam/not spam, fraud/legit).
• Clustering groups similar records without pre-labeled classes (for example, customer segments).
• Natural Language Processing (NLP) works with text and language understanding.
• Computer Vision (CV) works with images and video.
• Generative AI (Gen-AI) creates new text, code, images, or other content from prompts.

Azure-oriented solution patterns

• Use regression when business output is numeric and precision matters.
• Use classification when outcomes are discrete labels and decisions need thresholds.
• Use clustering when you need discovery and pattern mining before explicit labels exist.
• Use NLP for chat, summarization, translation, extraction, and sentiment.
• Use CV for detection, OCR, scene understanding, and visual quality checks.
• Use Gen-AI for copilots, draft generation, retrieval-augmented chat, and workflow automation.

Quick decision checkpoints

1. What is the output type: number, label, group, text/vision understanding, or generated content?
2. Is training custom or can prebuilt Azure AI capabilities solve it faster?
3. Do you need explainability, low latency, or content controls from day one?

Pick the workload first; Azure service selection becomes much easier afterward.