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Duration 14 hours
Course Outline
Overview of GitHub Copilot
- Defining GitHub Copilot and its operational mechanics
- Compatible environments and IDE integrations
- Practical use cases for developers and DevOps specialists
Initial Setup with Copilot
- Activating Copilot within Visual Studio Code
- Crafting prompts to elicit valuable code suggestions from Copilot
- Evaluating and refining code generated by Copilot
Applying Copilot to DevOps Responsibilities
- Creating YAML configurations for CI/CD workflows
- Developing GitHub Actions with Copilot assistance
- Streamlining pipelines for testing, linting, and deployment
Shell Scripting and Infrastructure Automation
- Composing and enhancing shell scripts using Copilot
- Requesting Dockerfile, Terraform, or Kubernetes configuration snippets from Copilot
- Verifying the validity of generated automation scripts
Enhancing Productivity with AI Support
- Minimizing boilerplate code and repetitive duties
- Improving velocity with Copilot during agile sprints
- Integrating Copilot with GitHub CLI and terminal-based workflows
Constraints, Ethics, and Best Practices
- Grasping the scope and boundaries of Copilot's capabilities
- Addressing security risks and intellectual property issues
- Recommended methods for reviewing AI-generated code
Practical Projects and Real-World Applications
- Automating CI/CD workflows for web applications
- Developing reusable GitHub Action templates
- Facilitating team collaboration using Copilot across multiple repositories
Recap and Future Directions
Requirements
- Fundamental knowledge of software development principles
- Basic familiarity with Git or version control systems
- Initial experience with YAML, shell scripting, or CI/CD tools
Target Audience
- Developers seeking to enhance DevOps efficiency
- Novice DevOps practitioners and automation professionals
- Agile team members looking 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
