Google Professional Machine Learning Engineer
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You are tasked with developing a machine learning solution to classify a large volume of written support cases into three categories: Technical Support, Billing Support, or Other Issues. The solution must be developed, tested, and deployed swiftly to meet immediate business needs. Given the constraints of time and the requirement for high accuracy, which of the following pipeline configurations would you choose? Please select the best option that aligns with Google Cloud's best practices for rapid deployment and scalability. Choose one correct option.
A
Utilize the Cloud Natural Language API to extract metadata for categorizing the incoming cases, considering its ease of use and quick integration capabilities.
B
Develop a custom TensorFlow model leveraging Google’s BERT pre-trained model for high accuracy. After constructing and evaluating a classifier, deploy the model using Vertex AI for scalability and management.
C
Employ AutoML Natural Language to automatically create and assess a classifier, then deploy the model as a REST API for easy integration with existing systems, benefiting from its no-code approach and rapid deployment.
D
Implement BigQuery ML to design and test a logistic regression model for classifying incoming requests, and use BigQuery ML for predictions, taking advantage of its SQL-based interface for simplicity.