https://github.com/kingabzpro/a-to-z-mlops
A complete MLOps project.
https://github.com/kingabzpro/a-to-z-mlops
cicd docker docker-compose fastapi graphana kaggle locust machine-learning mlflow mlops prefect prometheus text-classification uv
Last synced: 5 months ago
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A complete MLOps project.
- Host: GitHub
- URL: https://github.com/kingabzpro/a-to-z-mlops
- Owner: kingabzpro
- License: mit
- Created: 2025-05-07T11:57:40.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-06-23T10:50:27.000Z (about 1 year ago)
- Last Synced: 2025-06-23T11:45:07.678Z (about 1 year ago)
- Topics: cicd, docker, docker-compose, fastapi, graphana, kaggle, locust, machine-learning, mlflow, mlops, prefect, prometheus, text-classification, uv
- Language: Jupyter Notebook
- Homepage:
- Size: 3.46 MB
- Stars: 1
- Watchers: 1
- Forks: 1
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 📰 News Classification MLOps
Production-ready MLOps pipeline for classifying news articles. Built with FastAPI, Docker, Kubernetes, and modern observability tools.
## What This Is
An end-to-end machine learning pipeline that ingests news data, trains classification models, and serves predictions through a REST API with full monitoring and orchestration.
[🚀 Quick Start](https://github.com/kingabzpro/A-to-Z-MLOps/wiki/Quick-Start) | [📚 Documentation](https://github.com/kingabzpro/A-to-Z-MLOps/wiki) | [📡 API Reference](https://github.com/kingabzpro/A-to-Z-MLOps/wiki/API-Reference)
## ✨ Key Capabilities
- **🔄 Automated Pipeline** - Data processing, model training, and deployment orchestrated with Prefect
- **⚡ Production API** - FastAPI with batch prediction, authentication, rate limiting, and caching
- **📊 Full Observability** - Prometheus metrics, Grafana dashboards, MLflow experiment tracking
- **🐳 Container-Ready** - Docker Compose for local development, Kubernetes for production
- **🧪 Comprehensive Testing** - Unit, integration, and load testing with Locust
## 🚀 Quick Start
```bash
git clone https://github.com/kingabzpro/A-to-Z-MLOps.git
cd A-to-Z-MLOps
cp .env.example .env # Add your API_KEY and Kaggle credentials
docker-compose up -d
```
Access the services:
- **🔥 API & Docs**: http://localhost:7860/docs
- **📈 MLflow Tracking**: http://localhost:5000
- **📉 Grafana Dashboards**: http://localhost:3000
- **🔍 Prometheus Metrics**: http://localhost:9090
- **⚙️ Prefect Orchestration**: http://localhost:4200
Test the API:
```bash
curl -X POST "http://localhost:7860/predict" \
-H "X-API-Key: your_api_key" \
-H "Content-Type: application/json" \
-d '{"title": "Apple releases new iPhone with AI features"}'
```
## 📚 Documentation
Detailed guides available in the [Wiki](https://github.com/kingabzpro/A-to-Z-MLOps/wiki):
- 🚀 [Quick Start](https://github.com/kingabzpro/A-to-Z-MLOps/wiki/Quick-Start) - Complete setup instructions
- 📁 [Project Structure](https://github.com/kingabzpro/A-to-Z-MLOps/wiki/Project-Structure) - Codebase organization
- 📡 [API Reference](https://github.com/kingabzpro/A-to-Z-MLOps/wiki/API-Reference) - All endpoints documented
- ⚙️ [Configuration](https://github.com/kingabzpro/A-to-Z-MLOps/wiki/Configuration) - Environment variables
- 📊 [Monitoring](https://github.com/kingabzpro/A-to-Z-MLOps/wiki/Monitoring) - Observability setup
- ☁️ [Cloud Deployment](https://github.com/kingabzpro/A-to-Z-MLOps/wiki/Cloud-Deployment) - AWS, GKE, AKS guides
- 🛠️ [Development](https://github.com/kingabzpro/A-to-Z-MLOps/wiki/Development) - Contributing guidelines
## 🤝 Contributing
See the [Development Guide](https://github.com/kingabzpro/A-to-Z-MLOps/wiki/Development) for contribution workflow and coding standards.
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MIT License - Created for the MLOps community