Lilly Tech Systems
Learn AWS Cost Optimization for AI
Master strategies to reduce your AI/ML infrastructure costs on AWS by up to 70%. Learn Spot instances, Savings Plans, right-sizing, and monitoring techniques for GPU and accelerator workloads.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
Understand AI/ML cost drivers on AWS, the cost optimization framework, and common pitfalls that lead to overspending.
2. Spot Instances
Leverage Spot instances for training workloads with checkpointing, interruption handling, and fleet diversification.
3. Savings Plans
Choose between Compute, EC2, and SageMaker Savings Plans for predictable AI workloads and reserved capacity.
4. Right-Sizing
Select optimal instance types, GPU configurations, and resource allocations based on workload profiling.
5. Monitoring
Set up cost dashboards, alerts, budgets, and anomaly detection for AI/ML spend using AWS Cost Explorer and tools.
6. Best Practices
Organizational strategies, FinOps for AI, governance frameworks, and continuous optimization processes.
What You'll Learn
By the end of this course, you'll be able to:
Reduce GPU Costs
Save up to 90% on training costs using Spot instances with proper checkpointing and interruption handling.
Plan Commitments
Choose the right Savings Plans and Reserved Instances for your AI workload patterns and budget.
Right-Size Resources
Profile workloads and select optimal instance types to eliminate waste and maximize GPU utilization.
Monitor Spend
Build dashboards and alerts that catch cost anomalies before they become budget-breaking surprises.