Vertex AI bundles services that cover data to deployment: Datasets for labeling and validation; Training via custom jobs and AutoML; Hyperparameter Tuning for automated search; Model Registry for versioning and lineage; Endpoints for online serving; Batch Prediction for offline inference; Pipelines for orchestration; Model Monitoring for drift and performance; and integrations with TensorBoard, Cloud Logging, and Cloud Monitoring. It also includes access to foundation models for generation and embeddings through managed APIs.
These services are designed to interoperate. A typical flow: create or import a dataset, kick off a training job (custom container or AutoML), log metrics to TensorBoard, register the model in the Registry, deploy to an Endpoint, and set up Monitoring with alerts. Pipelines automate this across environments (dev/stage/prod), while IAM sculpts who can run or promote which artifacts. Everything is accessible via SDKs, REST, or console, making it scriptable and CI/CD-friendly.
For vector-driven systems, two pieces are especially relevant: embedding generation (either with a hosted model or your own) and orchestration to keep indexes fresh. You can run batch jobs that generate embeddings at scale and push them to Milvus, schedule periodic re-embeddings via Pipelines, and feed retrieval results into online endpoints for ranking or generation. With these services, you don’t need to build a bespoke platform to achieve RAG, semantic search, or agent memory; you assemble standard Vertex AI components around Milvus as the vector backbone.