Content
By the end of the course, students will be able to:
- Explain core cloud computing concepts and the AWS global infrastructure model
- Launch and configure fundamental AWS services (EC2, S3, RDS, Lambda)
- Build and deploy a serverless application using AWS Lambda, API Gateway, and DynamoDB
- Apply DevOps principles using AWS CodePipeline and AWS CloudFormation
- Describe responsible AI principles and demonstrate basic use of AWS AI/ML services
- Identify AWS certification pathways relevant to their area of study
Learning objectives
This intensive 20-hour course introduces students to cloud computing on Amazon Web Services, progressing from foundational concepts to hands-on application development. Students will gain practical experience with core AWS services, serverless architectures, and modern DevOps practices through a combination of instructor-led sessions and guided lab exercises. The course is designed to be immediately applicable — students leave with working cloud projects and a clear pathway to AWS certification.
Day 1 — Cloud Foundations (4 hours) Cloud computing concepts and why they matter; AWS global infrastructure (Regions, Availability Zones); core AWS services overview (EC2, S3, RDS, Lambda); AWS Free Tier and student resources; AWS certification pathways. Hands-on lab: launch first EC2 instance, create S3 bucket, explore AWS Console.
Day 2 — Culture of Innovation and Working at AWS (2 hours) Amazon Leadership Principles; working backwards from customer needs; two-pizza teams and ownership culture; innovation mechanisms (Press Release/FAQ); career paths at AWS. Interactive exercise: students draft a press release for a hypothetical cloud service.
Day 3 — Serverless Application Development (6 hours) Serverless computing concepts; AWS Lambda functions and triggers; Amazon API Gateway for REST APIs; Amazon DynamoDB for NoSQL data; AWS SAM (Serverless Application Model) for deployment. Hands-on labs: build a REST API with Lambda and API Gateway; create a serverless web application; deploy using AWS SAM CLI.
Day 4 — DevOps and CI/CD on AWS (4 hours) DevOps principles and practices; AWS CodePipeline for continuous integration and delivery; AWS CodeBuild and CodeDeploy; Infrastructure as Code with AWS CloudFormation; monitoring with Amazon CloudWatch. Hands-on lab: create a CI/CD pipeline to automatically deploy application changes.
Day 5 — AI/ML Fundamentals and Course Wrap-Up (4 hours) Machine learning concepts and use cases; Amazon SageMaker for ML model development; pre-trained AI services (Rekognition, Comprehend, Translate); ML workflow overview; responsible AI principles. Hands-on lab: build and deploy an image classification model using SageMaker. Course wrap-up: certification pathways, AWS Educate registration, Q&A.
Prerequisites
Students should have a basic understanding of programming concepts (any language); familiarity with command-line interfaces is helpful but not required. No prior cloud experience is needed. Days 3–4 (Serverless and DevOps) are most accessible to students with Python, Node.js, or Java experience.