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Duration 14 hours
Course Outline
Introduction to GitHub Copilot
- Defining GitHub Copilot and explaining its underlying mechanisms
- Exploring supported environments and IDE integrations
- Identifying key use cases for both developers and DevOps professionals
Getting Started with Copilot
- Activating Copilot within Visual Studio Code
- Crafting effective prompts to elicit valuable code suggestions
- Evaluating and refining code generated by Copilot
Applying Copilot to DevOps Tasks
- Creating YAML configurations for CI/CD workflows
- Developing GitHub Actions with Copilot's assistance
- Automating testing, linting, and deployment pipelines
Shell Scripting and Infrastructure Automation
- Leveraging Copilot to author and optimize shell scripts
- Generating Dockerfile, Terraform, or Kubernetes configuration snippets via prompts
- Verifying the accuracy and security of generated automation scripts
Enhancing Productivity with AI Assistance
- Minimizing boilerplate code and repetitive chores
- Improving speed and efficiency during agile sprints using Copilot
- Integrating Copilot with GitHub CLI and terminal-based workflows
Limitations, Ethics, and Best Practices
- Grasping the scope and inherent boundaries of Copilot
- Addressing security concerns and intellectual property implications
- Adopting best practices for reviewing AI-generated code
Project Exercises and Real-World Scenarios
- Automating the CI/CD workflow for a web application
- Developing reusable GitHub Actions templates
- Facilitating team collaboration using Copilot across multiple repositories
Summary and Next Steps
Requirements
- A solid grasp of fundamental software development principles
- Basic proficiency with Git or general version control workflows
- Introductory experience with YAML, shell scripting, or CI/CD tooling
Target Audience
- Developers aiming to elevate their DevOps productivity
- DevOps newcomers and automation enthusiasts
- Agile team members looking to integrate AI support into their daily workflows
Testimonials (1)
That i gained a knowledge regarding streamlit library from python and for sure i'll try to use it to improve applications in my team which are made in R shiny
