You work for a large technology company that wants to modernize their contact center. You have been asked to develop a solution to classify incoming calls by product so that requests can be more quickly routed to the correct support team. You have already transcribed the calls using the Speech-to-Text API. You want to minimize data preprocessing and development time. How should you build the model?
To classify incoming calls by product effectively while minimizing data preprocessing and development time, you should use AutoML Natural Language. This tool is designed to extract custom entities and also provides easy-to-use interfaces for training custom models tailored to your specific needs without extensive technical expertise or complex setup. It is the most suitable option for quickly developing a specialized classification system for your company's unique products.
Should be B -> minimize data preprocessing and development time
Agree its B. A and D is incorrect since it requires more development time. C is also incorrect since the product is company specific and might not be well recognized by Cloud Natural Language API.
I thought the answer is B too. However, after carefully reading the question and answers again, B produces entities for classification only, not a classification result. So, A and D are only candidates and A is better.
AutoML Natural Language - custom entities, with least development time
I'm leaning towards C over B here. The question is underlining that minimal development time is required, and C is even less than B. If the information is really domain specific, then you'd need B, but it's not clear what products the company sells, so we don't have enough info to say it's too domain specific for C.
If anything, C is wrong because it tells you something that is not true: extract custom entities with Natural Language API it's not possible. That is something you can do only with AutoML. Look at this comparison table: https://cloud.google.com/natural-language#section-6 That's how they subtly point you at answer B.
B- automl custom classification and entity is going to help with minimum effort.
I'm voting C here!
Should be B Basic classification, entity extraction, and sentiment analysis are available through the Cloud Natural Language API. AutoML Natural Language enables you to define custom classification categories, entities, and sentiment scores that are relevant to your application.
no. if you need custom entities you don't use APIs
"minimize data preprocessing and development time" answer will be limited to B,C will choose C as Natural Language API does not handle custom operation
Went with B
C) Cloud NLP API
AutoML is appropriate to classify incoming calls by product (Custom) to be routed to the correct support team. Cloud Natural Language API is for general case (not particular business)
why not C?
you have to classify company products, which are custom classes
you can still use Option C (Cloud Natural Language API) even when the solution needs to classify incoming calls by company-specific products rather than general products. The Cloud Natural Language API can be customized to handle company-specific entities and classifications effectively.
Key Differences: Approach: Option B (AutoML Natural Language) involves using an AutoML service to train a custom NLP model, while Option C (Cloud Natural Language API) relies on a pre-built NLP API. Control and Customization: Option B gives you more control and customization over the training process, as you train a model specific to your needs. Option C offers less control but is quicker to set up since it uses a pre-built API. Complexity: Option B might require more technical expertise to set up and configure the AutoML model, while Option C is more straightforward and user-friendly. In summary, both options allow you to extract custom entities for classification, but Option B (AutoML) involves more manual involvement in training a custom model, while Option C (Cloud Natural Language API) provides a simpler, pre-built solution
AutoML only has classification and regression