Full course coming soon

Infrastructure Planning for AI Workloads

7 chapters · 32 lessons

1. 1. From Workload to Infrastructure Requirements

   4 lessons

   Learn to translate AI workload characteristics into measurable infrastructure demands.

2. 2. Power and Cooling as Strategic Constraints

   5 lessons

   Understand how electrical and thermal systems limit compute capacity and deployment speed.

3. 3. Evaluating Data Center Options

   5 lessons

   Compare colocation, cloud regions, and dedicated facilities using operational and economic criteria.

4. 4. Workload Orchestration and Utilization

   4 lessons

   Design scheduling strategies that maximize compute utilization within power and thermal limits.

5. 5. Capacity Planning and Forecasting

   5 lessons

   Build capacity models that align AI roadmaps with infrastructure timelines and budgets.

6. 6. Operational Risk and Resilience

   4 lessons

   Identify and mitigate infrastructure risks that threaten AI service availability and performance.

7. 7. Cross-Functional Infrastructure Decisions

   5 lessons

   Navigate vendor negotiations, contract terms, and stakeholder alignment for infrastructure investments.