What should be monitored to gauge the performance of machine learning systems?

Monitoring latency is crucial for gauging the performance of machine learning systems because it directly impacts user experience and the system's responsiveness. Latency refers to the time taken for the system to respond to a given input or request. In the context of machine learning, high latency can indicate inefficiencies in data processing, model inference times, or issues with underlying infrastructure.

By measuring latency, engineers can identify bottlenecks in the workflow, adjust resource allocation, or optimize algorithms to ensure that users receive timely results. Consistently monitoring latency helps in maintaining service levels and enhances the overall efficiency and usability of the machine learning application.

While database size, power consumption, and the programming language used can provide insights into various aspects of system performance or resource management, they are not direct measures of how quickly or effectively a machine learning model performs its tasks. Observing latency remains the most relevant metric for assessing real-time performance in this context.