Which two strategies are most critical for effective monitoring in an AI data center?

Implementing predictive maintenance based on historical hardware performance data is a critical strategy for effective monitoring in an AI data center. This approach allows the data center to proactively address potential hardware failures before they occur, thus minimizing downtime and ensuring continuous operational efficiency. By analyzing historical performance data, operators can identify patterns and trends that may indicate an impending issue, such as hardware degradation or failures. This foresight enables timely interventions, resulting in enhanced reliability and performance of the AI infrastructure.

Additionally, the choice of deploying a comprehensive monitoring system that includes real-time metrics on CPU, GPU, memory, and network usage is also vital. Real-time monitoring supplies operators with instantaneous data about the performance and health of the systems, allowing for swift response to any anomalies. This constant oversight helps maintain optimal operational effectiveness and can provide insights into system efficiency, enabling resource allocation to be adjusted if necessary.

The other strategies, while they may have their uses, do not align with the best practices for maintaining a robust and effective monitoring system in a data center setting. Manual logging can lead to delayed insights and is prone to human error, while disabling non-essential monitoring can overlook critical metrics that may indicate future problems, potentially jeopardizing the overall performance and reliability of the infrastructure. Therefore, the focus should remain