Monitoring and logging refer to the processes of tracking and recording the performance and behavior of AI systems and applications. Monitoring involves real-time observation of system metrics, such as response times, error rates, and resource utilization, while logging captures detailed records of events and transactions that occur within the system. These practices are essential for maintaining system health, troubleshooting issues, and ensuring compliance with operational standards. Common use cases include tracking model performance in production, identifying anomalies in data processing, and auditing user interactions in AI applications.
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