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Duration 14 hours
Course Outline
Introduction to GitHub Copilot
- Defining GitHub Copilot and explaining its operational mechanism
- Overview of supported environments and IDE integration
- Exploring use cases relevant to developers and DevOps experts
Initial Setup with Copilot
- Activating Copilot in Visual Studio Code
- Crafting prompts to elicit useful code suggestions from Copilot
- Interpreting and refining code generated by Copilot
Applying Copilot to DevOps Challenges
- Creating YAML configurations for CI/CD workflows
- Developing GitHub Actions with the assistance of Copilot
- Automating processes for testing, linting, and deployment pipelines
Shell Scripting and Infrastructure Automation
- Leveraging Copilot to draft and enhance shell scripts
- Requesting Dockerfile, Terraform, or Kubernetes configuration snippets from Copilot
- Validating the accuracy and reliability of generated automation scripts
Enhancing Productivity with AI Support
- Minimizing boilerplate code and repetitive duties
- Accelerating work within agile sprints using Copilot
- Integrating Copilot with GitHub CLI and terminal-based workflows
Constraints, Ethics, and Best Practices
- Recognizing the scope and limitations of Copilot
- Addressing security issues and intellectual property implications
- Establishing best practices for reviewing AI-generated code
Practical Exercises and Real-World Scenarios
- Automating the CI/CD workflow for a web application
- Developing reusable GitHub Actions templates
- Facilitating team collaboration using Copilot across multiple repositories
Recap and Future Steps
Requirements
- A solid grasp of fundamental software development principles
- Experience with Git or general version control workflows
- Foundational knowledge of YAML, shell scripting, or CI/CD tools
Target Audience
- Developers seeking to elevate their DevOps efficiency
- Emerging DevOps professionals and automation enthusiasts
- Members of Agile teams 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
