You have recently used TensorFlow to train a classification model on tabular data. You have created a Dataflow pipeline that can transform several terabytes of data into training or prediction dataset

While Vertex AI Pipelines can orchestrate batch predictions, reusing an existing Dataflow pipeline for transformation and prediction is generally more efficient and direct than reimplementing or wrapping it within a pipeline component if the Dataflow job is already robust.

Importing the model into Vertex AI and deploying it to an endpoint provides managed model serving. An existing Dataflow pipeline can then be orchestrated to perform batch predictions from the deployed model on a weekly schedule, leveraging its existing data transformation logic, and then writing the results directly to BigQuery.

Similar to A, using Vertex AI Pipelines for the entire process might be an option, but the question implies an existing Dataflow pipeline, making a direct Dataflow job for prediction a more straightforward and efficient path.

Importing a custom TensorFlow model directly into BigQuery ML is not the standard approach for external TensorFlow models, and reimplementing data processing in SQL would negate the existing Dataflow pipeline.