Implement Computer Vision Solutions
Services and verbs
Azure AI Vision Image Analysis returns captions, dense captions, tags, objects with bounding boxes, people, smart crops and text. OCR (the Read model) extracts printed and handwritten text as lines and words with bounding polygons; it is asynchronous for larger documents and returns a readResults/analyzeResult structure you must parse. Face performs detection plus verification, identification and find-similar; identification and 1:N search are Limited Access features requiring an approved registration form, while basic detection is generally available.
Custom Vision
Create a project whose type is fixed at creation: classification (multiclass - one label per image; multilabel - several) or object detection (needs region-normalised bounding boxes). Upload and tag images, train an iteration, review precision, recall and mAP, then publish the iteration to a prediction resource. Exported models (ONNX, TensorFlow, CoreML, Docker) run offline on edge devices.
Practical constraints
Images must respect format and size limits; large files and batch jobs need the async path; keys and endpoints differ per resource type (training vs prediction for Custom Vision).
Traps and rule of thumb
Questions confuse multiclass with multilabel, assume Face identification works without approval, or treat OCR as a synchronous single call for a multi-page PDF. Another common trap: analysing an image with the prediction key when a separate training key is needed.
Rule of thumb: if the question says "which region of the image", think object detection; if it says "what is this image about", think classification; if it says "approval/registration", think Face.