Automating ML Feature Pipelines: How Vertex AI Bridges Training-Serving Skew

Training-serving skew—when training data and production inference data diverge—is a persistent challenge in ML operations that leads to model degradation. Vertex AI Feature Store solves this with a unified architecture: BigQuery serves as the authoritative offline store while Bigtable provides sub-millisecond latency for online inference, with automatic synchronization between the two. By implementing this architecture as Terraform infrastructure-as-code, teams can ensure reproducible, scalable feature serving across environments.