Question

A retail analytics team needs to score 400 million customer records once per week using a trained TensorFlow model deployed on Vertex AI. The records live in a large BigQuery table. Latency is not a concern, but the team wants the job to complete as quickly and cheaply as possible while avoiding the operational overhead of managing infrastructure. Which approach best meets these requirements?

Accepted Answer

Run a Vertex AI batch prediction job that reads directly from the BigQuery table as the input source and writes results back to BigQuery, letting the service distribute the workload.

Answer

Deploy the model to a Vertex AI online endpoint with autoscaling and send the 400 million records as individual prediction requests from a Dataflow job.

Answer

Export the BigQuery table to a single large CSV file in Cloud Storage and run a batch prediction job pointed at that file.

Answer

Provision a large GPU-backed Compute Engine VM, load the model, and iterate through the records with a custom Python loop.