Exam Professional Machine Learning Engineer topic 1 question 68 discussion

You do not have enough data to train a model from scratch. AutoML needs training data.

custom modeling needed

A) AutoML - Codeless

"Quickly build" >> usually go with the low-code/no-code options of autoML

AutoML does not have transfer learning capabilities as of now. Given that there are not enough data to train from scratch, B is the only option that makes sense.

This suitable job for AutoML, it used transfer learning when there is small data for training.

AutoML Natural Language is designed to work well even with relatively small datasets. It uses transfer learning and other techniques to train models effectively on limited data, which is crucial since there's enough data to train a model from scratch.

Custom models and custom categories and hence AutoML natural language, It would still work with less data

NO DATA TO TRAIN THE MODEL FROM SCRACH

Went with A

It's A. "Custom categories" means B can't be correct

Its A, Check this document, https://cloud.google.com/natural-language/automl/docs/beginners-guide The Natural Language API discovers syntax, entities, and sentiment in text, and classifies text into a predefined set of categories.

If you do not have enough data to train a model from scratch, then it may be more appropriate to use a pre-trained model or a pre-made Jupyter Notebook. Option B, the Cloud Natural Language API, could still be a viable option if you have access to labeled data for sentiment analysis. The API provides pre-trained models for sentiment analysis that you can use to classify text. However, if you have custom categories or labels, then you would need to train a custom model, which may not be feasible with limited data.

https://www.toptal.com/machine-learning/google-nlp-tutorial#:~:text=Google%20Natural%20Language%20API%20vs.&text=Google%20AutoML%20Natural%20Language%20is,t%20require%20machine%20learning%20knowledge. In this case need custom categories without writing code

Quickly ==> A and B and custom categories + you do not have enough data to train a model (it doesn't mean no data to train) it will probably have a few samples Let's say 10 samples) as this link https://cloud.google.com/natural-language/automl/docs/beginners-guide#include-enough-labeled-examples-in-each-category ==> A

Quickly ==> A and B and custom categories + you do not have enough data to train a model (it doesn't mean no data to train) it will probably have a few samples Let's say 10 samples) ==> B

B is the correct answer AutoML needs data for training and its clearly mentioned we don't have any data.