1. Default detection methods for numerical and categorical data are the same as for other functions, with customization available for precision monitoring needs.
2. Unified model monitoring approach for online and batch serving using BigQuery’s model monitoring functions for models deployed on Vertex AI Prediction Endpoints or batch serving data stored in BigQuery.
3. Automation is essential for truly scalable monitoring of shifts and drifts, leveraging BigQuery’s procedural language for streamlining processes such as continuous model retraining and proactive identification of data quality issues.
The default detection methods for numerical and categorical data, as well as thresholds, can be customized for precision monitoring needs in BigQuery’s model monitoring functions. The accompanying tutorial provides a detailed demonstration of how these metrics are calculated manually and compares them to the results obtained using the function for validation.
When it comes to online and batch serving data, BigQuery’s model monitoring functions offer a unified approach for monitoring models deployed on Vertex AI Prediction Endpoints or using batch serving data stored within BigQuery. For batch serving, monitoring features can be easily accessed for prediction data already stored in or accessible by BigQuery. For online serving, models deployed on Vertex AI Prediction Endpoints can be monitored by configuring logging requests and responses to BigQuery to detect skew and drift.
The tutorial includes a step-by-step guide on endpoint creation, model deployment, logging setup, and monitoring both online and batch serving data within BigQuery. To achieve scalable monitoring of shifts and drifts, automation is essential. BigQuery’s procedural language enables automation, not only for monitoring but also for continuous model retraining. In a production environment, automation can help in proactively identifying data quality issues, adapting to real-world changes, and maintaining a deployment strategy aligned with organizational needs.