Vertex AI integrates seamlessly with BigQuery and Dataflow to support end-to-end machine learning workflows that span data preparation, model training, and prediction. BigQuery acts as a scalable data warehouse for storing and querying large structured datasets, while Dataflow provides a managed Apache Beam service for transforming and processing streaming or batch data. Vertex AI connects to both services through its datasets and pipelines APIs, allowing developers to move data efficiently from storage and transformation stages into model training and inference.
For example, you can query features directly from BigQuery using SQL and export them into a Vertex AI dataset for training. This integration allows you to perform complex aggregations and joins at scale without manual data movement. Dataflow can then be used to preprocess raw data—such as tokenizing text, normalizing values, or generating embeddings—and write the cleaned results into Cloud Storage or BigQuery for use by Vertex AI. This pipeline ensures consistency between the data used for training and production, which is critical for maintaining model reliability.
In more advanced setups, Vertex AI can write back prediction results to BigQuery or Dataflow pipelines. When embedding models are used, Milvus fits naturally into this flow by providing a vector retrieval layer between Dataflow and Vertex AI. For instance, embeddings generated in Vertex AI can be stored in Milvus, and Dataflow can orchestrate continuous updates or re-indexing operations. BigQuery then serves as the analytics layer, enabling SQL-based analysis of retrieval performance or user interaction logs. This three-way integration—Vertex AI for modeling, Dataflow for transformation, and BigQuery for analytics—creates a complete, production-ready data and AI ecosystem.