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Answer: Call the Cloud Natural Language API from your application. Process the generated Entity Analysis as labels.
The correct answer is A. Given the constraints of time, lack of resources, and lack of machine learning expertise in the team, using an existing service like the Cloud Natural Language API is the best choice. Entity Analysis can automatically generate subject labels based on the content, which is quicker and does not require building and training custom models. Sentiment Analysis, on the other hand, is used to understand the sentiment expressed in the text, which is not suitable for generating subject labels. Options C and D involve building and deploying custom models, which require more time and expertise, thus not suitable for the given constraints.
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You are tasked with developing an application on Google Cloud that will automatically generate subject labels for users' blog posts. Given the competitive landscape and the urgency to implement this feature swiftly, you have no additional developer resources available, and your team lacks experience in machine learning. What approach should you take to achieve this objective?
A
Call the Cloud Natural Language API from your application. Process the generated Entity Analysis as labels.
B
Call the Cloud Natural Language API from your application. Process the generated Sentiment Analysis as labels.
C
Build and train a text classification model using TensorFlow. Deploy the model using Cloud Machine Learning Engine. Call the model from your application and process the results as labels.
D
Build and train a text classification model using TensorFlow. Deploy the model using a Kubernetes Engine cluster. Call the model from your application and process the results as labels.
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