Lilly Tech Systems
AWS AI Infrastructure Best Practices Advanced
This final lesson applies the AWS Well-Architected Framework's ML lens to provide comprehensive best practices for operating AI infrastructure on AWS. These practices cover operational excellence, security, reliability, performance, and cost optimization.
AWS Well-Architected ML Checklist
Checklist
OPERATIONAL EXCELLENCE: [ ] Infrastructure as Code (CloudFormation/Terraform) for all ML resources [ ] CI/CD pipeline for model deployment (CodePipeline + SageMaker) [ ] CloudWatch dashboards for GPU utilization, training progress [ ] Automated alerting on training failures and model degradation [ ] Runbooks for common ML infrastructure issues SECURITY: [ ] Least-privilege IAM roles with permission boundaries [ ] VPC endpoints for all AWS service access [ ] KMS encryption for S3 data and model artifacts [ ] CloudTrail logging for all ML API calls [ ] SCP guardrails in AWS Organizations RELIABILITY: [ ] Multi-AZ deployment for inference endpoints [ ] Checkpointing for all training jobs [ ] Auto-recovery for GPU instance failures [ ] Model rollback capability for serving endpoints COST OPTIMIZATION: [ ] Spot instances for training (60-90% savings) [ ] Savings Plans for steady-state inference [ ] S3 lifecycle policies for training data tiering [ ] Right-sized instances based on utilization data
AWS Cost Optimization for ML
| Strategy | Savings | Best For |
|---|---|---|
| Spot Instances | 60-90% | Training, batch inference |
| Savings Plans (Compute) | 20-40% | Steady-state inference |
| Inferentia2 | 40% vs GPU | Inference (supported models) |
| Trainium | 50% vs GPU | Training (supported frameworks) |
| S3 Intelligent-Tiering | 30-60% | Infrequently accessed training data |
Course Complete: You now have comprehensive knowledge of building AI infrastructure on AWS. Combine these practices with the broader cloud AI patterns from other courses to build world-class ML platforms on Amazon Web Services.
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