Exam Professional Data Engineer topic 1 question 148 discussion

Should be B.

AutoML is used to train model and do damage detection Auto Vision is used is a pre trained model used to detect objects in images

as per chat gpt One of the features of Cloud Vision API is damage detection, which can be used to identify and classify various types of damage in images, such as cracks, dents, scratches, stains, etc

For this scenario, where you need to automate the detection of damaged packages in real time while they are in transit, the most suitable solution among the provided options would be B. Here's why this option is the most appropriate: Real-Time Analysis: AutoML provides the capability to train a custom model specifically tailored to recognize patterns of damage in packages. This model can process images in real-time, which is essential in your scenario. Integration with Existing Systems: By building an API around the AutoML model, you can seamlessly integrate this solution with your existing package tracking applications. This ensures that the system can flag damaged packages for human review efficiently. Customization and Accuracy: Since the model is trained on your specific corpus of images, it can be more accurate in detecting damages relevant to your use case compared to pre-trained models.

I was leaning towards C but tested out uploading some damaged boxes to Vision API. It seems to have a lot of trouble detecting damaged boxes. It mislabeled boxes as a tire or toy. Also, there is no part of the API that seems to be able to detect damage. So I'll have to go with B. You should train a model to accomplish this then integrate with your app.

AutoML for Custom Models: AutoML (Auto Machine Learning) is designed to simplify the process of training custom machine learning models, including image classification models. It allows you to leverage Google Cloud's pre-built AutoML Vision service to train a model specifically for detecting package damage based on your corpus of images. This ensures accurate and customized results. Real-time API Integration: After training the AutoML model, you can create an API endpoint that integrates seamlessly with your package tracking applications. This means that as packages move on the delivery lines, you can send images in real-time to the API for immediate analysis. Scalability: AutoML Vision is built to scale, so it can handle the analysis of images in real-time, even as packages move continuously on the delivery lines.

Keywords: realtime, camera streaming https://cloud.google.com/vision#:~:text=where%20you%20are-,Vertex%20AI%20Vision,-Vertex%C2%A0AI%20Vision Option B AutoML would be too complex and not time efficient. Using Vision AI(Vertex AI Vision) first + AutoML Option D is better than B (just AutoML).

B https://www.cloudskillsboost.google/focuses/22020?parent=catalog

Option B - AutoML

will stay with B. Might be more reliable, accurate. as many says in duscission, Vision Api does not say it has defect detection. i remember labs with Auto ML, where models were trained. Vertex AI labs.

B is right

B C-is not answer Vision API currently allows you to use the following features: https://cloud.google.com/vision/docs/features-list#:~:text=Vision%20API%20currently%20allows%20you%20to%20use%20the%20following%20features%3A

It looks like B is the only valid option, with the assumption that you have a corpus of images (the question does not say that you do not). It would not be Cloud Vision API because that does not do damage detection (https://cloud.google.com/vision/docs/features-list).

https://cloud.google.com/solutions/visual-inspection-ai#all-features

Here it is mentioned that the company is planning to implement a camera system. So it does not have the training data yet. Without having training data , the only option left is to use pre- trained models through cloud API . C Is the answer. B is wrong as you dont have data to train the model.

AutoML is used to train model and do damage detection Auto Vision is used is a pre trained model used to detect objects in images

Cloud Vision API -> pre-trained models to detect labels, faces, words AutoML -> custom specific models trained for specific use case