This specialization provides a practical introduction to AI-powered software development, testing, and deployment using leading developer tools such as GitHub Copilot, Amazon Q Developer, Windsurf, and Cursor. Learn to generate, debug, refactor, test, and deploy code, apply prompt engineering, build Python and JavaScript projects, automate CI/CD workflows, and improve software quality and deployment efficiency with Generative AI.
By the end of this program, you will be able to:
- Master AI Coding Tools: Use GitHub Copilot, Amazon Q Developer, Windsurf, and Cursor for AI-assisted development.
- Build and Debug Code: Generate, refactor, debug, and validate code while developing APIs and Python projects.
- Improve Software Testing: Generate test cases, optimize regression testing, detect bugs, and enhance testing workflows with GenAI.
- Automate Software Deployment: Apply Docker, Terraform, IaC, CI/CD, infrastructure monitoring, and AI-assisted deployment practices.
Ideal for beginners, students, developers, programmers, software testers, QA professionals, DevOps professionals, and aspiring AI developers looking to build practical Generative AI and AI-powered developer tool skills.
Applied Learning Project
Project Overview: Build, Test, and Deploy a Secure Customer Feedback API
In this project, you'll take a Customer Feedback API from requirements to deployment using AI coding assistants. You'll use AI to accelerate coding, testing, debugging, and deployment while applying human review, validation, and security practices throughout the development process.
Project tasks include:
- Define API Requirements and Acceptance Criteria
- Build the API Using AI Coding Assistants
- Implement Input Validation
- Create and Debug Automated Tests
- Containerize the API with Docker
- Set Up CI/CD Testing
- Create Infrastructure as Code
- Conduct a Security and Deployment Readiness Review
