Monitoring deployed models in Vertex AI is essential to ensure they remain accurate, stable, and efficient after deployment. Vertex AI provides built-in tools like Model Monitoring that track prediction data, detect data drift, and alert developers to anomalies. Developers can configure baseline datasets during deployment so that future predictions are continuously compared against expected distributions. This helps identify when the input data changes significantly from training data—an early indicator of degraded performance or concept drift.
In addition to data drift, Vertex AI can monitor prediction quality metrics such as accuracy, precision, or recall by using labeled evaluation data. Logs from model endpoints capture request counts, latency, and errors, all accessible through Cloud Logging and Cloud Monitoring dashboards. Developers can set up custom metrics and alerts using these tools to detect when a model starts failing business-level KPIs, such as increased false positives or slower inference times. These observability capabilities integrate with Google Cloud Operations Suite, allowing centralized visibility across multiple deployed endpoints.
For systems that integrate with Milvus or other retrieval layers, monitoring also includes embedding quality and retrieval latency. Developers should log similarity scores, average query times, and retrieval accuracy from Milvus to ensure the vector database contributes to model performance effectively. For example, if the embedding drift increases or search latency spikes, the overall agent quality in a Vertex AI application may degrade. Regular monitoring of both the model and the vector layer ensures that the AI system maintains reliability and delivers consistent, accurate responses in production.