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Master cloud infrastructure for ML — from EC2 GPU instances and S3 storage to IAM security, VPC networking, serverless inference, SageMaker training, and cost optimization strategies.
Cloud computing transformed ML from a discipline limited by hardware budgets to one limited only by ideas. Before the cloud, training a large model required purchasing expensive GPU servers and waiting weeks for delivery. Today, you can launch 8x A100 GPUs in under a minute, train for a few hours, and tear them down — paying only for what you used.
This chapter covers the AWS services that every ML engineer needs to know. You will learn to launch GPU instances (EC2) for training, store datasets and model artifacts (S3), secure access with IAM policies, build isolated networks (VPC), deploy serverless inference endpoints (Lambda), use managed training infrastructure (SageMaker), and keep costs under control with billing alerts and spot instances.
The goal is practical fluency: by the end of this chapter, you will be able to take any ML project from your laptop to the cloud — training on GPU instances, storing checkpoints in S3, and deploying models as scalable endpoints — while keeping costs predictable and infrastructure secure.
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Why cloud beats on-prem for ML — pay-per-use compute, elastic scaling, regions, AZs, and the shared responsibility model.
EC2 runs the GPUs, S3 stores the data and models
GPU instance families (p3/p4/g5), AMIs, EBS volumes, spot vs on-demand, and how to right-size training nodes.
Object storage for datasets and checkpoints — buckets, prefixes, storage classes, lifecycle rules, and 11-nines durability.
IAM controls who, VPC controls where
Users, roles, policies, instance profiles — least-privilege access for training jobs and inference endpoints.
Private subnets, security groups, NAT gateways, VPC endpoints — isolating training clusters from the public internet.
Serverless for light traffic, SageMaker for managed ML
Event-driven inference, API Gateway, cold starts, container images — the cheapest path to production for low-traffic models.
Container registries for ML images, managed training jobs, hyperparameter tuning, and one-click model deployment.
Billing alerts, cost allocation tags, spot instances, and savings plans — keeping the AWS bill under control.
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