Vertex AI simplifies training by giving you managed training jobs, prebuilt frameworks, and automatic logging without owning cluster orchestration. You can submit a custom training job using TensorFlow, PyTorch, or a custom container, pick CPU/GPU/TPU counts, and Vertex AI handles scheduling, autoscaling, retries, and artifacts. Hyperparameter tuning runs parallel trials with Bayesian or grid strategies and writes metrics to a central store. For data, you typically read from Cloud Storage or BigQuery, log to TensorBoard, and export a saved model or custom inference container.
Deployment is equally direct. You upload a model artifact or container to the registry and deploy it to a Vertex AI Endpoint with the machine types and accelerators you need. Endpoints auto-scale with traffic, expose REST/gRPC, and integrate with Cloud Logging and Monitoring. You can set request/response schemas, add custom prediction code for pre/post-processing, and configure minimum/maximum replica counts. For batch inference, you submit a batch prediction job pointing at input files or tables; Vertex AI distributes the work and writes outputs back to Cloud Storage.
This approach pairs well with vector-based applications. For example, you can train an embedding model (supervised or contrastive) in a managed job, export it, and deploy it for online embedding generation. Ingest your corpus offline, generate embeddings in a batch prediction job, and upsert them to Milvus with your chosen index (IVF or HNSW). At query time, call the deployed embedding endpoint, search Milvus for top-k candidates, and then call a generation endpoint for an answer grounded by retrieved context. Vertex AI handles the compute lifecycle; Milvus handles fast semantic retrieval; your code stays focused on features and evaluation.