Google Professional Machine Learning Engineer
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In the context of deploying a machine learning model on Google's AI Platform for high-throughput online predictions, where the model requires computationally expensive preprocessing operations identical to those used during training, which architecture ensures scalability, cost-effectiveness, and low latency? Consider the following constraints: the solution must handle a high volume of requests in real-time, minimize operational overhead, and avoid the need for retraining the model with raw data. Choose the best option from the following:
A
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 AI Platform using the transformed data. Write the predictions to an outbound Pub/Sub queue.
B
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 AI Platform for online prediction.
C
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 AI Platform using the transformed data. Write the predictions to an outbound Pub/Sub queue.
D
Send incoming prediction requests to a Pub/Sub topic. Transform the incoming data using a Dataflow job. Submit a prediction request to AI Platform using the transformed data. Write the predictions to an outbound Pub/Sub queue.