Monitoring machine learning models in production generates significant amounts of data and requires systems that can operate reliably under load. Having identified what to monitor – from data drift to performance degradation – the focus now shifts to the practical aspects of building and managing the infrastructure needed to support these monitoring activities effectively at scale.

This chapter addresses the engineering challenges involved. You will learn about:

• Strategies for logging prediction data and monitoring outputs in high-volume environments.
• Using time-series databases ($TSDBs$) specifically designed for handling timestamped metric data efficiently.
• Designing distributed system architectures for monitoring pipelines that can scale horizontally.
• Integrating your monitoring components with common MLOps platforms like Kubeflow, MLflow, and SageMaker.
• An overview of specialized open-source and commercial tools available for ML monitoring.
• Techniques for creating informative dashboards and setting up meaningful alerts to stay informed about model health.

We will examine how to select and configure these components to create a monitoring system tailored to the demands of production machine learning.