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Duration 14 hours
Course Outline
Introduction to GitHub Copilot
- Overview of GitHub Copilot and its underlying mechanics.
- Supported environments and IDE integration capabilities.
- Practical use cases for developers and DevOps professionals.
Getting Started with Copilot
- Setting up and enabling Copilot in Visual Studio Code.
- Crafting effective prompts to elicit useful code suggestions.
- Understanding and refining code generated by Copilot.
Applying Copilot to DevOps Tasks
- Generating YAML configurations for CI/CD workflows.
- Authoring GitHub Actions with Copilot assistance.
- Automating testing, linting, and deployment pipelines.
Shell Scripting and Infrastructure Automation
- Leveraging Copilot to write and optimize shell scripts.
- Requesting Dockerfile, Terraform, or Kubernetes configuration snippets via prompts.
- Validating and reviewing generated automation scripts.
Enhancing Productivity with AI Assistance
- Minimizing boilerplate code and repetitive tasks.
- Increasing velocity with Copilot during agile sprints.
- Integrating Copilot with GitHub CLI and terminal-based workflows.
Limitations, Ethics, and Best Practices
- Grasping the scope and boundaries of Copilot's capabilities.
- Addressing security concerns and intellectual property implications.
- 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 Next Steps
Requirements
- Foundational knowledge of basic software development concepts.
- Familiarity with Git or other version control workflows.
- Basic hands-on experience with YAML, shell scripting, or CI/CD tools.
Target Audience
- Developers aiming to enhance their DevOps productivity.
- DevOps newcomers and automation enthusiasts.
- 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
