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Duration 14 hours
Course Outline
Overview of GitHub Copilot
- Definition of GitHub Copilot and its operational mechanics
- Compatible environments and IDE integrations
- Practical applications for developers and DevOps experts
Initiating Work with Copilot
- Activating Copilot in Visual Studio Code
- Crafting effective prompts to obtain valuable code suggestions
- Analyzing and refining code generated by Copilot
Applying Copilot to DevOps Operations
- Creating YAML configurations for CI/CD processes
- Developing GitHub Actions with the aid of Copilot
- Streamlining pipelines for testing, linting, and deployment
Shell Scripting and Infrastructure Automation
- Leveraging Copilot to develop and optimize shell scripts
- Requesting Dockerfile, Terraform, or Kubernetes configuration snippets from Copilot
- Verifying the accuracy of generated automation scripts
Enhancing Productivity through AI Support
- Minimizing boilerplate code and repetitive chores
- Increasing velocity during agile sprints using Copilot
- Integrating Copilot with GitHub CLI and terminal-based workflows
Constraints, Ethics, and Best Practices
- Grasping the scope and limitations of Copilot
- Addressing security risks and intellectual property issues
- Adopting best practices for auditing AI-generated code
Project Exercises and Practical Scenarios
- Automating the CI/CD workflow for a web application
- Developing reusable templates for GitHub Actions
- Facilitating team collaboration with Copilot across multiple repositories
Recap and Future Directions
Requirements
- Foundational knowledge of core software development principles
- Experience with Git or version control systems
- Introductory familiarity with YAML, shell scripting, or CI/CD tools
Target Audience
- Developers aiming to enhance their DevOps efficiency
- Newcomers to DevOps and those interested in automation
- 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