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Duration 14 hours
Course Outline
Introduction to GitHub Copilot
- Overview of GitHub Copilot and its underlying mechanisms
- Compatible environments and IDE integration options
- Practical use cases for developers and DevOps specialists
Initial Setup with Copilot
- Activating Copilot within Visual Studio Code
- Crafting effective prompts for valuable code suggestions
- Analyzing and refining code generated by Copilot
Applying Copilot to DevOps Responsibilities
- Creating YAML configurations for CI/CD processes
- Developing GitHub Actions with Copilot assistance
- Automating pipelines for testing, linting, and deployment
Shell Scripting and Infrastructure Management
- Utilizing Copilot to draft and optimize shell scripts
- Requesting snippets for Dockerfiles, Terraform, or Kubernetes configurations
- Verifying the accuracy of generated automation scripts
Enhancing Productivity through AI Support
- Minimizing boilerplate and repetitive coding tasks
- Accelerating workflow velocity in agile sprints using Copilot
- Integrating Copilot with GitHub CLI and terminal-based operations
Constraints, Ethics, and Industry Standards
- Comprehending the scope and boundaries of Copilot
- Addressing security risks and intellectual property implications
- Best practices for auditing AI-generated code
Practical Projects and Real-World Applications
- Automating CI/CD workflows for web applications
- Developing reusable GitHub Action templates
- Fostering team collaboration using Copilot across multiple repositories
Recap and Future Directions
Requirements
- A foundational grasp of core software development principles
- Working knowledge of Git or general version control workflows
- Entry-level experience with YAML, shell scripting, or CI/CD tooling
Target Audience
- Developers seeking to elevate their DevOps productivity
- Novices in DevOps and those passionate about automation
- Agile team members aiming to integrate AI support into 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
