You need to quickly build and train a model to predict the sentiment of customer reviews with custom categories without writing code. You do not have enough data to train a model from scratch. The resulting model should have high predictive performance. Which service should you use?
AutoML Natural Language is the best choice for quickly building and training a custom sentiment analysis model without writing code. It is specifically designed to work well with relatively small datasets and offers high predictive performance through techniques like transfer learning. The service allows users to train custom models that meet their specific needs, including custom categories, which aligns with the requirements of this question.
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.
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.
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.
B is the correct answer AutoML needs data for training and its clearly mentioned we don't have any data.
they said, "do not have enough data"!!!!
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
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
It's A. "Custom categories" means B can't be correct
NO DATA TO TRAIN THE MODEL FROM SCRACH
"You do not have enough data to train a model from scratch" - I think this means that there is SOME data but not a lot, something which AutoML can handle.
B is the correct answer. The API connects with the prebuilt Google NLP model for prediction
A wish0035 explained
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
https://cloud.google.com/natural-language/automl/docs/beginners-guide#include-enough-labeled-examples-in-each-category
Sorry, I go with A A A A A A
Went with A
Custom models and custom categories and hence AutoML natural language, It would still work with less data
This suitable job for AutoML, it used transfer learning when there is small data for training.
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.
https://cloud.google.com/vertex-ai/docs/text-data/sentiment-analysis/prepare-data
"Quickly build" >> usually go with the low-code/no-code options of autoML
A) AutoML - Codeless