Operational infrastructure for monitoring AI services, model usage, performance, errors, and system resources.
Monitoring can include logs, metrics, health checks, latency, resource usage, model behavior, and service availability.
These capabilities support reliable operation of online AI services, APIs, research platforms, and internal model deployments.
Implementation is selected according to the available data, technical constraints, required level of automation, and the existing software or research environment. The solution can be implemented as a standalone component or integrated into a larger system.
The resulting system is intended to provide a clear computational workflow that can be evaluated, maintained, and extended as the project develops. Model choice, data processing, interfaces, and deployment can therefore be adapted to the requirements of the specific project.