What is Resource Utilization Monitoring?

Success metrics include system uptime, model performance stability, deployment velocity, and operational cost efficiency.

Track GPU utilization and memory for inference and training workloads. Track CPU usage for preprocessing and feature engineering. Monitor memory consumption for data loading and feature stores. Track network I/O for distributed training and feature retrieval. Track storage I/O for checkpointing and data loading. Compare actual utilization against allocated resources to identify overprovisioning. Set up dashboards showing utilization trends over time to inform capacity planning and cost optimization.

Start by identifying overprovisioned resources where utilization is consistently below 50%. Right-size in small increments of 10-20% reduction and monitor for performance impact. Use auto-scaling to match capacity to demand rather than provisioning for peak. Schedule batch workloads during serving off-peak hours. Share GPU resources between development workloads using time-slicing. Never optimize resource allocation during peak traffic periods. Maintain at least 20% headroom above typical peak utilization.

Target 60-80% sustained utilization for ML serving workloads. Below 60% indicates overprovisioning or suboptimal batching. Above 90% leaves insufficient headroom for traffic spikes and increases latency variance. For training workloads, target 90%+ utilization since these are batch jobs where latency doesn't matter. Monitor utilization at different time scales: instantaneous for autoscaling, hourly for capacity planning, and monthly for budgeting.

Track GPU utilization and memory for inference and training workloads. Track CPU usage for preprocessing and feature engineering. Monitor memory consumption for data loading and feature stores. Track network I/O for distributed training and feature retrieval. Track storage I/O for checkpointing and data loading. Compare actual utilization against allocated resources to identify overprovisioning. Set up dashboards showing utilization trends over time to inform capacity planning and cost optimization.

Start by identifying overprovisioned resources where utilization is consistently below 50%. Right-size in small increments of 10-20% reduction and monitor for performance impact. Use auto-scaling to match capacity to demand rather than provisioning for peak. Schedule batch workloads during serving off-peak hours. Share GPU resources between development workloads using time-slicing. Never optimize resource allocation during peak traffic periods. Maintain at least 20% headroom above typical peak utilization.

Target 60-80% sustained utilization for ML serving workloads. Below 60% indicates overprovisioning or suboptimal batching. Above 90% leaves insufficient headroom for traffic spikes and increases latency variance. For training workloads, target 90%+ utilization since these are batch jobs where latency doesn't matter. Monitor utilization at different time scales: instantaneous for autoscaling, hourly for capacity planning, and monthly for budgeting.

Track GPU utilization and memory for inference and training workloads. Track CPU usage for preprocessing and feature engineering. Monitor memory consumption for data loading and feature stores. Track network I/O for distributed training and feature retrieval. Track storage I/O for checkpointing and data loading. Compare actual utilization against allocated resources to identify overprovisioning. Set up dashboards showing utilization trends over time to inform capacity planning and cost optimization.

Start by identifying overprovisioned resources where utilization is consistently below 50%. Right-size in small increments of 10-20% reduction and monitor for performance impact. Use auto-scaling to match capacity to demand rather than provisioning for peak. Schedule batch workloads during serving off-peak hours. Share GPU resources between development workloads using time-slicing. Never optimize resource allocation during peak traffic periods. Maintain at least 20% headroom above typical peak utilization.

Target 60-80% sustained utilization for ML serving workloads. Below 60% indicates overprovisioning or suboptimal batching. Above 90% leaves insufficient headroom for traffic spikes and increases latency variance. For training workloads, target 90%+ utilization since these are batch jobs where latency doesn't matter. Monitor utilization at different time scales: instantaneous for autoscaling, hourly for capacity planning, and monthly for budgeting.