resource optimization

Explores how to right-size VMs for AI workloads by profiling training vs. inference, balancing GPUs, CPUs, memory, storage, and networking. Covers autoscaling, placement, and cost optimization, forecasting demand with telemetry, and using benchmarking and capacity models to ensure performance, reliability, and efficiency.

Explores how AI elevates virtual machine monitoring: ingesting telemetry at scale, learning normal behavior, spotting anomalies, forecasting capacity, and triggering safe, automated remediation. It outlines architecture patterns, key VM metrics, SLO-driven alerts, security signal correlation, and rollout best practices to cut costs, boost reliability, and reduce operational toil.

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An overview of using AI to modernize Linux capacity planning. It shows how to collect CPU, memory, disk, and network telemetry, train time‑series and anomaly models to forecast demand, right‑size resources, and trigger autoscaling. It compares open‑source stacks, warns about data quality, seasonality, and drift, and outlines a practical rollout with MLOps, alerts, and cost controls.