Which architecture should you use?

You have trained a model on a dataset that required computationally expensive preprocessing operations. You need to execute the same preprocessing at prediction time. You deployed the model on Al Platform for high-throughput online prediction.

Which architecture should you use?
A . • Validate the accuracy of the model that you trained on preprocessed data
• Create a new model that uses the raw data and is available in real time
• Deploy the new model onto Al Platform for online prediction
B . • Send incoming prediction requests to a Pub/Sub topic
• Transform the incoming data using a Dataflow job
• Submit a prediction request to Al Platform using the transformed data
• Write the predictions to an outbound Pub/Sub queue
C . • Stream incoming prediction request data into Cloud Spanner
• Create a view to abstract your preprocessing logic.
• Query the view every second for new records
• Submit a prediction request to Al Platform using the transformed data
• Write the predictions to an outbound Pub/Sub queue.
D . • Send incoming prediction requests to a Pub/Sub topic
• Set up a Cloud Function that is triggered when messages are published to the Pub/Sub topic.
• Implement your preprocessing logic in the Cloud Function
• Submit a prediction request to Al Platform using the transformed data
• Write the predictions to an outbound Pub/Sub queue

View Answer

Answer: B

Explanation:

https://cloud.google.com/architecture/data-preprocessing-for-ml-with-tf-transform-pt1#where_to_do_preprocessing