AI + DevOps Projects
This section showcases a selection of real-world projects where I integrated artificial intelligence into DevOps and SRE workflows.
These are not concepts — they are working implementations tested in practical use cases.
Over the past few years, I’ve explored how LLMs (Large Language Models) can support DevOps engineers in repetitive, analytical, and decision-making tasks.
🤖 What does “DevOps + AI” mean?
DevOps + AI is the natural evolution of automation:
- CI/CD pipelines enhanced by language models
- Automatic test generation from source code
- Semantic code quality analysis
- Real-time correlation of metrics, logs, and alerts
- Automated reporting and intelligent insights for teams
🔍 Code Quality Notifier
A system that analyzes Pull Requests and automatically generates comments using LLMs (GPT-4), focusing on style, complexity, and potential issues.
- 🔗 View on GitHub
- 🛠️ Tech stack: GitHub Actions, GPT, Prometheus, Grafana, Loki, Python
🧪 AI-based Test Generator
Generates unit and functional tests using AI models capable of understanding code semantics.
- 🔗 Live Demo
- 🛠️ Tech stack: Python, Streamlit, OpenAI API, GitHub CI
📊 GitOps + AI Dashboard
An intelligent dashboard to monitor DevOps metrics, code quality, and security, with AI-generated alerts and summaries.
- 🔗 View on GitHub
- 🛠️ Tech stack: Prometheus, Grafana, GitHub Webhook, GPT-4
🧠 Perspective
The goal is not to replace engineers, but to augment them.
AI in DevOps is not magic — it’s a tool. And like any powerful tool, it works best in the hands of someone who knows what they want to achieve.
📫 For demos, collaboration or questions:
massimo.danieli@gmail.com