Vertex AI handles large-scale machine learning workflows by combining managed infrastructure, orchestration, and data integration into a single pipeline system. It provides Vertex Pipelines, a component built on Kubeflow Pipelines, to automate multi-step processes such as data preprocessing, training, evaluation, and deployment. Each step runs as a containerized component, allowing developers to scale different parts of the workflow independently. This is especially useful for teams training models on terabytes of data or running multiple experiments in parallel.
A common pattern involves integrating Vertex AI with Google Cloud services like BigQuery for data ingestion and Cloud Storage for intermediate outputs. Training jobs can be distributed across GPU or TPU clusters, while the model artifacts are versioned and logged automatically. The system also supports parallel hyperparameter tuning jobs to efficiently search through configuration spaces. These capabilities make it possible to manage large, repeatable workflows that can be re-run with minimal manual intervention—ideal for production-grade ML pipelines that require traceability and scalability.
When combined with Milvus, large-scale workflows can extend beyond training into semantic retrieval. For instance, after a model produces millions of embeddings, those can be stored and indexed in Milvus for downstream applications like search or clustering. Milvus’s distributed indexing ensures that embedding retrieval remains efficient even as datasets grow. Together, Vertex AI and Milvus provide a full-stack architecture for large-scale ML—Vertex AI for orchestration and computation, Milvus for persistent vector storage and search—making the workflow both data-intensive and operationally manageable.