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Duration 14 hours
Course Outline
Introduction to GitHub Copilot
- Overview of GitHub Copilot and its underlying mechanisms
- Supported environments and integration with IDEs
- Practical use cases for developers and DevOps specialists
Getting Started with Copilot
- Activating Copilot in Visual Studio Code
- Crafting prompts to elicit valuable code suggestions from Copilot
- Interpreting and refining code generated by Copilot
Applying Copilot to DevOps Tasks
- Creating YAML configurations for CI/CD workflows
- Developing GitHub Actions with the assistance of Copilot
- Streamlining testing, linting, and deployment pipelines
Shell Scripting and Infrastructure Automation
- Employing Copilot to author and enhance shell scripts
- Requesting snippets for Dockerfiles, Terraform, or Kubernetes configurations from Copilot
- Verifying the accuracy and reliability of generated automation scripts
Enhancing Productivity with AI Assistance
- Minimizing boilerplate code and repetitive duties
- Achieving greater speed when using Copilot within agile sprints
- Integrating Copilot with GitHub CLI and terminal-based workflows
Limitations, Ethics, and Best Practices
- Defining the scope and boundaries of Copilot's capabilities
- Addressing security concerns and intellectual property issues
- Adopting best practices for auditing code produced by AI
Project Exercises and Real-World Scenarios
- Automating CI/CD workflows for a web application
- Constructing reusable GitHub Action templates
- Facilitating team collaboration using Copilot across multiple repositories
Summary and Next Steps
Requirements
- A solid grasp of fundamental software development principles
- Proficiency with Git or other version control systems
- Foundational experience with YAML, shell scripting, or CI/CD tools
Target Audience
- Developers aiming to elevate their DevOps efficiency
- Beginners in DevOps and enthusiasts of automation
- Members of agile teams looking to incorporate AI support into their 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
