You've heard that everything is moving to the cloud, but until you can explain exactly what that means and why it matters, it's just noise. In this module, you'll build a working model of cloud computing from the ground up — distinguishing service delivery models, mapping AWS's global infrastructure, and taking your first steps toward a secure AWS account.

Choosing how to run your applications in the cloud is one of the most consequential decisions you'll make — and the wrong fit costs money, performance, or both. In this module, you'll evaluate the three primary AWS compute models (virtual servers, serverless functions, and containers) so you can match each to the right workload and deployment scenario.

Your data has to live somewhere, and the wrong storage choice can cost you performance, money, or both. In this module, you'll compare Amazon EBS, Amazon EFS, and Amazon S3 across their core capabilities so you can match each service to the workload it was designed for.

The moment you deploy resources on AWS, you face a set of decisions about how traffic reaches them, who can access them, and how quickly content is delivered to users around the world. In this module, you'll configure AWS networking fundamentals — from private cloud architecture to global content delivery — to ensure your resources are reachable, resilient, and fast.

Not every application stores data the same way, and choosing the wrong database can be as costly as choosing no database at all. In this module, you'll compare Amazon RDS and Amazon DynamoDB across their data models, scaling approaches, and access patterns so you can justify which service fits a given application's requirements.

The ability to extract insight from data — and act on it at machine speed — has shifted from a competitive advantage to a baseline expectation for modern organizations. In this module, you'll explore how AWS analytics services turn raw data into queryable datasets, how SageMaker gives teams the infrastructure to build and deploy machine learning models, and how AWS's pre-built AI services apply intelligence directly to content and language without requiring you to train a model from scratch.

When one part of your application fails, the rest of it shouldn't have to stop too. In this module, you'll use Amazon SQS and Amazon SNS to decouple application components with message queuing and publish-subscribe notifications, so services can communicate reliably without being tightly bound to each other's availability.

Every resource you deploy on AWS is only as secure as the permissions controlling who can reach it and what they can do when they get there. In this module, you'll apply AWS Identity and Access Management to enforce the principle of least privilege, and configure compliance and threat detection services to protect your account from unauthorized access and policy violations at scale.

Manual infrastructure provisioning introduces inconsistency, human error, and no audit trail. In this module, you'll use AWS CloudFormation to define infrastructure as code, Amazon CloudWatch to monitor running resources and trigger automated responses, and AWS Budgets to set cost alerts before unexpected spending becomes a billing problem.

You've covered the foundational building blocks of AWS — compute, storage, networking, databases, analytics, messaging, security, and management. But AWS currently offers more than 200 services, and what you've built here is the vocabulary and mental model you'll need to evaluate what comes next. In this module, you'll take stock of what you've learned, identify where to go deeper, and leave with a framework for continuing your AWS education at the right pace for your role.