A manufacturing organization has a large collection of images labeled as intact or defective parts. They want to use this data to build a simple solution to detect faulty parts on their production line. They have no data science expertise. Which solution should they use?
Correct Answer:
D
The correct answer is D. AutoML. Here's why:
Context of the Questio n : The organization wants to build a solution to detect faulty parts on a production line using a large collection of labeled images (intact or defective parts). They do not have data science expertise, so they need a tool that simplifies the machine learning process.
Google Cloud Product Relevance:
AutoML is a suite of machine learning products that allows users to build custom models easily without needing deep expertise in machine learning or data science. It is designed to simplify the process of training, evaluating, and deploying models, especially in cases like image recognition where labeled datasets are already available.
AutoML Vision, a part of AutoML, would be ideal for this use case as it specifically handles image classification and can easily differentiate between intact and defective parts.
Why Not Other Options:
A . Discovery AI for Retail: This is a solution tailored for retail use cases like product discovery and search optimization, not for manufacturing defect detection.
B . Pre-trained APIs: While pre-trained APIs (like Vision API) can recognize general image patterns, they may not be specific enough for a custom use case like detecting defective parts.
C . Document AI: This service is designed for understanding and processing documents, not for analyzing images.
Google Cloud Digital Leader Reference:
For more information on AutoML, refer to the AutoML Vision documentation.
