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Duration 21 hours
Course Outline
Introduction to Graphite and Contemporary Code Review Workflows
- An overview of Graphite’s architecture and its core capabilities
- Exploring stacked pull requests and workflow automation
- Setting up Graphite with GitHub for collaborative team projects
Installing and Configuring Graphite
- Deploying Graphite within development environments
- Linking repositories and overseeing permission management
- Setting up merge queues, PR inboxes, and code review policies
Enhancing Pull Request Workflows
- Implementing stacked PRs and tracking dependencies
- Minimizing merge conflicts while accelerating review processes
- Managing large codebases using Graphite’s review framework
Leveraging AI for Code Review and Productivity Gains
- Utilizing Graphite’s AI code review assistant
- Incorporating open-source LLMs such as Deepseek, Qwen, and Mistral Small for deeper code insights
- Generating automated suggestions and enforcing quality standards
Integrating Graphite into DevOps Toolchains
- Connecting Graphite with CI/CD pipelines
- Integrating with GitHub Actions, Jenkins, and other automation platforms
- Maintaining compliance and auditability within enterprise workflows
Analytics, Metrics, and Reporting
- Utilizing Graphite dashboards to track team performance
- Pinpointing bottlenecks and process inefficiencies
- Creating custom reports and data visualizations
Scaling Graphite for Enterprise Use
- Establishing multi-team setups and governance frameworks
- Adopting best practices for large-scale implementation
- Addressing security, data retention, and compliance requirements
Practical Workshop: End-to-End Implementation
- Constructing a complete enterprise-grade Graphite workflow
- Incorporating AI-based review pipelines
- Performing team performance analysis and planning improvements
Recap and Future Directions
Requirements
- A solid grasp of Git-based workflows
- Practical experience with software development and version control systems
- Working knowledge of code review and CI/CD concepts
Intended Audience
- Engineering leads and software development managers
- DevOps and platform engineering teams
- Senior developers and technical architects
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
