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Duration 14 hours
Course Outline
Introduction to GitHub Copilot
- Overview of GitHub Copilot and its operational mechanics
- Compatible environments and IDE integration details
- Practical use cases for developers and DevOps engineers
Initial Setup with Copilot
- Activating Copilot within Visual Studio Code
- Crafting effective prompts for optimal code suggestions
- Reviewing and refining AI-generated code
Applying Copilot to DevOps Workflows
- Generating YAML configurations for CI/CD pipelines
- Developing GitHub Actions with Copilot assistance
- Automating testing, linting, and deployment processes
Shell Scripting and Infrastructure Automation
- Utilizing Copilot to author and optimize shell scripts
- Prompting for snippets related to Dockerfiles, Terraform, or Kubernetes configs
- Validating the accuracy of generated automation scripts
Enhancing Productivity with AI Support
- Minimizing boilerplate and repetitive coding tasks
- Improving speed and efficiency during agile sprints
- Integrating Copilot with GitHub CLI and terminal-based workflows
Constraints, Ethics, and Best Practices
- Defining the scope and boundaries of Copilot’s capabilities
- Addressing security issues and intellectual property concerns
- Establishing best practices for auditing AI-generated code
Practical Exercises and Real-World Scenarios
- Automating CI/CD workflows for web applications
- Creating reusable GitHub Actions templates
- Facilitating team collaboration across repositories using Copilot
Conclusion and Future Directions
Requirements
- Foundational knowledge of core software development principles
- Working familiarity with Git or other version control workflows
- Basic proficiency in YAML, shell scripting, or CI/CD tooling
Target Audience
- Developers aiming to enhance their DevOps efficiency
- DevOps newcomers and those passionate about automation
- Agile team members seeking AI assistance in 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