Use Vertex AI for deep learning when you want managed infrastructure, flexible training options, and integrated deployment without building your own MLOps stack. If your project needs GPUs/TPUs, distributed training, hyperparameter tuning, and reproducible artifacts, Vertex AI’s training and registry services are a good fit. When you reach serving, endpoints provide autoscaling, traffic splitting, and monitoring—key for models that will see variable or growing traffic. The platform is especially useful for teams that need to iterate quickly while maintaining auditability and controls.
If your workload involves multimodal or text/image tasks, you can combine custom architectures with foundation models and shared operational tooling. For example, pretrain or fine-tune a vision model in a custom job, log metrics to TensorBoard, register the best checkpoint, and deploy to an endpoint with GPUs. Use pipelines to automate data ingestion and evaluation, and monitoring to detect drift. This workflow scales from prototype to production without switching platforms.
For vector-based DL systems—RAG, semantic search, recommendations—Vertex AI pairs well with Milvus. Train or host embedding models in Vertex AI, generate embeddings offline for your corpus, and store them in Milvus with IVF or HNSW indexes. At query time, call an embedding endpoint, retrieve top-k candidates from Milvus with metadata filters, and send the context to a generator or classifier endpoint. Pipelines manage re-embedding and index refresh as your data evolves. Choose Vertex AI when you value this managed, composable approach that lets you focus on modeling and evaluation, while Milvus provides the high-performance vector layer.