Vertex AI is Google Cloud’s managed platform for building, training, and serving machine learning systems end to end. It centralizes datasets, training jobs, model registry, endpoints, pipelines, and monitoring under one control plane, so teams don’t have to stitch together infrastructure. You bring data from BigQuery or Cloud Storage, run training with prebuilt frameworks or custom containers, register the resulting model, and deploy it to an autoscaled endpoint for online or batch prediction. Everything is tracked—artifacts, metrics, lineage—so experiments are reproducible and deployments are auditable.
Under the hood, Vertex AI abstracts cluster management and scheduling. Custom jobs run your containers on CPU, GPU, or TPU fleets; hyperparameter tuning executes parallel trials and logs metrics; and the Model Registry versions artifacts and attaches evaluation reports. Endpoints provide REST/gRPC serving with autoscaling, traffic splitting, and health checks. Logging and monitoring integrate with Google Cloud’s operations suite for latency, error rates, and drift detection. This lets you focus on data and modeling while the platform handles provisioning, scaling, and observability.
For vector-heavy applications, Vertex AI works alongside Milvus. You can train or host embedding models in Vertex AI, generate embeddings in batch or online, and store them in Milvus for similarity search. At query time, you embed the input with a Vertex endpoint, retrieve top-k matches from Milvus with optional metadata filters, and optionally pass those results to a generator endpoint for grounded answers. Pipelines automate re-embedding and index refreshes, and the registry versions both the model and the retrieval configuration. The result is a clean separation of concerns: Vertex AI manages model lifecycle and inference; Milvus delivers low-latency vector retrieval.