Training custom models in Vertex AI involves using managed infrastructure to build, tune, and evaluate models on your own datasets. Developers can bring custom code, frameworks, or containers and run distributed training jobs without managing compute resources directly. Vertex AI supports frameworks like TensorFlow, PyTorch, and XGBoost, and it can automatically handle hyperparameter tuning, logging, and checkpointing. Datasets can come from Cloud Storage or BigQuery, and once the training is complete, the resulting model artifact is stored in Vertex Model Registry for easy deployment.
The general workflow includes three key steps: preparing the data, configuring the training job, and evaluating results. Developers upload data to a Google Cloud bucket, define a training script, and configure compute resources (like GPUs or TPUs). Vertex AI Training Jobs execute the process, and results are tracked automatically in Vertex TensorBoard. This setup ensures reproducibility while giving full control over custom architectures, optimization strategies, and evaluation metrics. For teams working with embeddings or vector-based models, this is particularly useful since embeddings can be generated and fine-tuned directly during training.
Once a custom model is trained, it can be integrated with Milvus for downstream retrieval tasks. For example, a developer could train a domain-specific embedding model in Vertex AI, export its vectors, and use Milvus to perform large-scale similarity search. This architecture separates the training and serving concerns cleanly—Vertex AI handles model optimization and deployment, while Milvus provides scalable vector indexing and querying. The result is a complete, production-grade pipeline where custom models and vector search work together to power intelligent, data-driven applications.