Exam Professional Data Engineer topic 1 question 126 discussion

B - You only need a PoC and it has be done quickly

Correct - A

The key difference between Google Cloud Vision AutoML and Cloud Vision API is that Cloud Vision API provides pre-trained models for basic image analysis tasks like object detection and labeling, while Cloud Vision AutoML allows you to train custom machine learning models to identify specific objects or concepts within images that are unique to your dataset, requiring you to provide labeled training data.

AutoML Vision is deprecated since march 31, 2024. The question will refer to Vertex AI AutoML. And as bet practice, the minimum dataset size for each label is 1000. So, with an updated question, the answer would be A.

A. Use Cloud Vision AutoML with the existing dataset. Here's why this is the most suitable option: Speed and Ease: AutoML simplifies model building. You simply upload your labeled images, and AutoML takes care of model selection, training, and evaluation. Existing Dataset Sufficiency: Your dataset (750 components x 1000 images each) is a decent starting point for AutoML, allowing you to quickly test its effectiveness. Minimal Custom Development: AutoML's out-of-the-box deployment options let you integrate the model into your app without extensive coding.

Option B is the fastest way to train a model that can be used to recognize the 750 different components.

Whats wrong with C, its fast, cheap and add your 750 labels which is not big work. AutoML is good to train on big dataset and costly as compared to APIs

A - https://cloud.google.com/vertex-ai/docs/beginner/beginners-guide Target at least 1000 examples per target

First I think in Vision API, but that is a pre-trained AI, will not recognize my labels, so because you have 1000 samples per item, AUTO ML is perfect. B cannot be because have not sensed to reduce your dataset if you have the recommended number of info. https://cloud.google.com/vision/automl/docs/beginners-guide#include_enough_labeled_examples_in_each_category The bare minimum required by AutoML Vision training is 100 image examples per category/label. The likelihood of successfully recognizing a label goes up with the number of high quality examples for each; in general, the more labeled data you can bring to the training process, the better your model will be. Target at least 1000 examples per label.

A is the answer. https://cloud.google.com/vision/automl/docs/beginners-guide#include_enough_labeled_examples_in_each_category The bare minimum required by AutoML Vision training is 100 image examples per category/label. The likelihood of successfully recognizing a label goes up with the number of high quality examples for each; in general, the more labeled data you can bring to the training process, the better your model will be. Target at least 1000 examples per label.

A - https://cloud.google.com/vision/automl/docs/beginners-guide#include_enough_labeled_examples_in_each_category

Based on this: "As a rule of thumb, we recommend to have at least 100 training samples per class if you have distinctive and few classes, and more than 200 training samples if the classes are more nuanced and you have more than 50 different classes" 750 different components = more than 50 different classes. That means we need more than 200 training samples. If we used 250 training samples out of the 1000 samples and multiply it to 750 different classes we get a total of 187,500 which is the equivalent of reducing the dataset twice. https://cloud.google.com/vision/automl/object-detection/docs/prepare#how_big_does_the_dataset_need_to_be

I choose A because on the vertex AI documentation (https://cloud.google.com/vertex-ai/docs/image-data/classification/prepare-data), on the best practices of preparing data for image recognition recommend this: We recommend about 1000 training images per label. The minimum per label is 10. In general, it takes more examples per label to train models with multiple labels per image, and resulting scores are harder to interpret. I know that is PoC, but if you do it without enough accuracy, you maybe discard the solution because it isn't fit for your requirements. So is better to do it with enough data to be sure that the model is or not accuracy enough with this data, because you maybe haven't enough accuracy and the problem is the quality of the data and not the amount of it.

https://cloud.google.com/vision/automl/docs/beginners-guide#include_enough_labeled_examples_in_each_category The bare minimum required by AutoML Vision training is 100 image examples per category/label. The likelihood of successfully recognizing a label goes up with the number of high quality examples for each; in general, the more labeled data you can bring to the training process, the better your model will be. Target at least 1000 examples per label.

750*1000 are a lot.

It is labeled, so A is correct

It's A. https://cloud.google.com/vision/automl/docs/beginners-guide#data_preparation The bare minimum required by AutoML Vision training is 100 image examples per category/label. The likelihood of successfully recognizing a label goes up with the number of high quality examples for each; in general, the more labeled data you can bring to the training process, the better your model will be. Target at least 1000 examples per label.