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Duration 14 hours
Course Outline
Introduction to GitHub Copilot
- Overview of GitHub Copilot and its operational mechanics
- Compatible environments and integration with IDEs
- Practical use cases for developers and DevOps specialists
Getting Started with Copilot
- Activating Copilot in Visual Studio Code
- Crafting effective prompts to elicit useful code suggestions
- Evaluating and refining code generated by Copilot
Leveraging Copilot for DevOps Tasks
- Creating YAML configurations for CI/CD workflows
- Developing GitHub Actions with Copilot assistance
- Automating testing, linting, and deployment pipelines
Shell Scripting and Infrastructure Automation
- Employing Copilot to author and optimize shell scripts
- Requesting Dockerfile, Terraform, or Kubernetes configuration snippets from Copilot
- Verifying the accuracy of generated automation scripts
Enhancing Productivity with AI Assistance
- Minimizing boilerplate code and repetitive tasks
- Improving workflow velocity during agile sprints using Copilot
- 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 considerations
- Adopting best practices for reviewing AI-generated code
Project Exercises and Real-World Scenarios
- Automating CI/CD workflows for a web application
- Creating reusable GitHub Actions templates
- Facilitating team collaboration using Copilot across multiple repositories
Summary and Future Directions
Requirements
- A foundational grasp of core software development principles
- Basic familiarity with Git or other version control systems
- Foundational experience with YAML, shell scripting, or CI/CD tools
Target Audience
- Developers seeking to enhance DevOps productivity
- Novice DevOps practitioners and automation enthusiasts
- Agile team members aiming to integrate 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