Question

A retail ML team preprocesses large volumes of transaction data for a churn model. During exploration in a Vertex AI Workbench notebook, engineers apply normalization and vocabulary-based categorical encoding using pandas. When the model is deployed, the online serving pipeline must apply the exact same transformations, but the team discovers subtle differences between training and serving preprocessing that degrade predictions. They want a scalable, reproducible approach that guarantees identical transformation logic in both batch training preprocessing and online serving. Which approach should they adopt?

Accepted Answer

Use TensorFlow Transform (tf.Transform) with Dataflow to compute preprocessing statistics over the full dataset and export a transform graph that is embedded into the serving signature.

Answer

Rewrite the pandas preprocessing as a Cloud Function that both the training job and the serving endpoint call over HTTP before inference.

Answer

Save the pandas preprocessing code in a shared Git repository and require both the training and serving teams to import the same module.

Answer

Precompute all transformed features in BigQuery and store both training and serving features in Vertex AI Feature Store without embedding transformations in the model.