A small manufacturing startup has only 200 photographs of its five proprietary machine part types and wants an image classifier that can tell the parts apart. The team has no data science staff and cannot collect thousands of additional images. Which Azure AI approach best fits this constraint?
Select an answer to reveal the explanation.
Short Explanation
You've got too few photos and no data science team to babysit a from-scratch model, so use Custom Vision with transfer learning. It starts from a pretrained model and learns your part types from a small labeled set. That's the practical Azure fit here.
Full Explanation
The correct answer is the Custom Vision service, using transfer learning to train a classifier from a small number of labeled images. The startup has only a small set of labeled images and lacks in-house data science expertise, and Custom Vision is designed for exactly this situation, using transfer learning on a pretrained model so that a small number of labeled images per category is enough to produce an accurate classifier without deep machine learning knowledge. The convolutional neural network built from scratch is incorrect because it typically demands a large labeled dataset and considerable machine learning expertise to design, train, and tune the network, which conflicts directly with the startup's stated constraints. The Azure AI Language custom text classification service is incorrect because it operates on written language, not photographs, so it cannot classify machine parts from images regardless of dataset size. The Anomaly Detector service is incorrect because it is built to find unusual patterns in time-series data such as sensor readings, not to categorize static photographs into part types.