Modern artificial intelligence workloads demand unprecedented compute density, pushing data center facilities to their physical and operational limits. This course bridges the gap between hardware engineering and cloud-based monitoring, equipping participants to design, sustain, and optimize infrastructure for GPU-intensive AI clusters.
The curriculum begins with power distribution and high-availability engineering. Participants learn to calculate and optimize Power Usage Effectiveness (PUE) using telemetry data from Microsoft Excel and Azure Monitor. Learners implement N+1 and 2N hardware redundancy topologies using Azure Resource Manager (ARM) templates to prevent downtime within strict power envelopes.
From power, the course transitions into precision thermal dynamics. Participants explore airflow physics, hot/cold aisle containment, and localized heat map generation in Excel using Azure IoT Central telemetry streams. The course examines GPU thermal throttling mechanics and teaches proactive mitigation strategies, including fan curve tuning and liquid-to-chip cooling loops, to maintain peak training performance.
Finally, participants will evaluate strategic infrastructure trade-offs and deploy automated protection networks. The curriculum compares on-premise liquid cooling against elastic cloud bursting, modeling Total Cost of Ownership (CapEx vs. OpEx) and data gravity constraints. Participants finish by deploying comprehensive sensor grids in Azure IoT Central and building automated incident response workflows with Azure Monitor, Logic Apps, and Microsoft Teams to preserve high-value AI hardware.
