Lilly Tech Systems
Introduction to AWS AI Infrastructure Beginner
Amazon Web Services offers the broadest and deepest set of AI/ML services of any cloud provider. From custom silicon (Trainium, Inferentia) to managed platforms (SageMaker) to foundation model APIs (Bedrock), AWS provides infrastructure for every stage of the ML lifecycle. This lesson maps the AWS AI ecosystem and helps you plan your infrastructure strategy.
AWS AI Service Stack
| Layer | Services | Target User |
|---|---|---|
| AI APIs | Rekognition, Comprehend, Translate, Polly, Transcribe | Application developers |
| Foundation Models | Bedrock, SageMaker JumpStart | App developers, ML engineers |
| ML Platform | SageMaker (Studio, Training, Endpoints) | Data scientists, ML engineers |
| Compute | EC2 (P5, G5, Inf2, Trn1), EKS, Lambda | Infrastructure engineers |
| Data | S3, Glue, EMR, Kinesis, Redshift | Data engineers |
Strategy Tip: Start with managed services (SageMaker, Bedrock) for speed to market. Move to self-managed infrastructure (EC2 + EKS) when you need more control, have specific cost requirements, or outgrow the managed service limitations.
AWS Custom AI Silicon
- AWS Trainium (Trn1) — Custom chip for training. Up to 50% cost savings vs GPU for supported frameworks. Use with Neuron SDK.
- AWS Inferentia2 (Inf2) — Custom chip for inference. Up to 40% better price-performance vs GPU. Supports PyTorch and TensorFlow.
- NVIDIA GPUs — P5 (H100), P4d (A100), G5 (A10G), G6 (L4) for maximum compatibility and performance.
Infrastructure Planning Checklist
- Region selection — Choose regions with GPU availability (us-east-1, us-west-2 have the most)
- Account structure — Separate ML accounts for training, serving, and data using AWS Organizations
- Networking — Dedicated VPC for ML workloads with VPC endpoints for S3, ECR, CloudWatch
- Cost controls — AWS Budgets, Cost Explorer, and Savings Plans for GPU reservations
Ready to Configure EC2 for ML?
The next lesson covers GPU instance families, EFA networking, and AMI configuration for ML workloads.
Next: EC2 for ML →