Google Vertex AI

Getting an ML model to production requires training infrastructure (GPU servers), a model store, a serving endpoint, and monitoring — each set up separately. Assembling the tools yourself makes infrastructure management a bigger burden than the ML research.

Previously, building ML models meant experimenting in Jupyter notebooks, managing separate GPU servers for training, and building serving APIs with Flask. Vertex AI emerged from the push to unify this fragmented workflow into a single platform.

Training data goes into a managed dataset. A training job runs on GPU clusters that Vertex AI provisions and tears down automatically. The trained model is stored in the Model Registry, where each version is tracked separately. From the registry, you deploy a model to an Endpoint, which auto-scales its serving instances based on prediction traffic. Once deployed, the Endpoint exposes a REST API that any other service can call to get predictions.

Vertex AI and BigQuery ML both run ML but take different approaches. BigQuery ML focuses on training simple models and running predictions inside SQL. Vertex AI covers the full ML lifecycle: large-scale custom training, AutoML, and model serving. If SQL-level ML is enough, use BigQuery ML; for full ML pipelines, use Vertex AI.

Ideal when you need to train and operate service-specific models for image/text classification, demand forecasting, recommendations, or anomaly detection. When you only need to call a pre-trained API for translation or sentiment analysis and have no custom training data, the full pipeline adds unnecessary complexity.