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Course Outline
Introduction to GitHub Copilot
- Overview of GitHub Copilot and its operational mechanisms
- Supported environments and IDE integrations
- Relevant use cases for developers and DevOps professionals
Getting Started with Copilot
- Enabling Copilot in Visual Studio Code
- Effectively prompting Copilot for code suggestions
- Reviewing and refining code generated by Copilot
Applying Copilot to DevOps Tasks
- Creating YAML configurations for CI/CD workflows
- Developing GitHub Actions with Copilot assistance
- Automating pipelines for testing, linting, and deployment
Shell Scripting and Infrastructure Automation
- Using Copilot to create and enhance shell scripts
- Generating snippets for Dockerfiles, Terraform, or Kubernetes configurations
- Validating automated scripts generated by AI
Enhancing Productivity with AI Support
- Minimizing boilerplate code and repetitive tasks
- Increasing speed and efficiency in agile sprints
- Integrating Copilot with GitHub CLI and terminal-based workflows
Limitations, Ethics, and Best Practices
- Understanding the scope and boundaries of Copilot
- Addressing security concerns and intellectual property issues
- Best practices for reviewing AI-generated code
Project Exercises and Real-World Scenarios
- Automating CI/CD workflows for web applications
- Creating reusable GitHub Actions templates
- Facilitating team collaboration using Copilot across repositories
Summary and Next Steps
Requirements
- A foundational understanding of software development concepts
- Familiarity with Git or version control workflows
- Basic experience with YAML, shell scripting, or CI/CD tools
Target Audience
- Developers seeking to enhance DevOps productivity
- DevOps newcomers and automation enthusiasts
- Agile team members looking for AI assistance in their workflows
14 Hours
Testimonials (2)
The session was highly interactive and applicable to the business.
Jorge Boscan - Chevron Global Technology Services Company
Course - Advanced GitHub Copilot & AI for Projects and Infrastructure
Machine Translated
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