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Built with FastAPI, Docker, Kubernetes, and modern observability tools.\n\n## What This Is\n\nAn 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.\n\n[🚀 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)\n\n## ✨ Key Capabilities\n\n- **🔄 Automated Pipeline** - Data processing, model training, and deployment orchestrated with Prefect\n- **⚡ Production API** - FastAPI with batch prediction, authentication, rate limiting, and caching\n- **📊 Full Observability** - Prometheus metrics, Grafana dashboards, MLflow experiment tracking\n- **🐳 Container-Ready** - Docker Compose for local development, Kubernetes for production\n- **🧪 Comprehensive Testing** - Unit, integration, and load testing with Locust\n\n## 🚀 Quick Start\n\n```bash\ngit clone https://github.com/kingabzpro/A-to-Z-MLOps.git\ncd A-to-Z-MLOps\ncp .env.example .env  # Add your API_KEY and Kaggle credentials\ndocker-compose up -d\n```\n\nAccess the services:\n- **🔥 API \u0026 Docs**: http://localhost:7860/docs\n- **📈 MLflow Tracking**: http://localhost:5000\n- **📉 Grafana Dashboards**: http://localhost:3000\n- **🔍 Prometheus Metrics**: http://localhost:9090\n- **⚙️ Prefect Orchestration**: http://localhost:4200\n\nTest the API:\n```bash\ncurl -X POST \"http://localhost:7860/predict\" \\\n  -H \"X-API-Key: your_api_key\" \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\"title\": \"Apple releases new iPhone with AI features\"}'\n```\n\n## 📚 Documentation\n\nDetailed guides available in the [Wiki](https://github.com/kingabzpro/A-to-Z-MLOps/wiki):\n\n- 🚀 [Quick Start](https://github.com/kingabzpro/A-to-Z-MLOps/wiki/Quick-Start) - Complete setup instructions\n- 📁 [Project Structure](https://github.com/kingabzpro/A-to-Z-MLOps/wiki/Project-Structure) - Codebase organization\n- 📡 [API Reference](https://github.com/kingabzpro/A-to-Z-MLOps/wiki/API-Reference) - All endpoints documented\n- ⚙️ [Configuration](https://github.com/kingabzpro/A-to-Z-MLOps/wiki/Configuration) - Environment variables\n- 📊 [Monitoring](https://github.com/kingabzpro/A-to-Z-MLOps/wiki/Monitoring) - Observability setup\n- ☁️ [Cloud Deployment](https://github.com/kingabzpro/A-to-Z-MLOps/wiki/Cloud-Deployment) - AWS, GKE, AKS guides\n- 🛠️ [Development](https://github.com/kingabzpro/A-to-Z-MLOps/wiki/Development) - Contributing guidelines\n\n## 🤝 Contributing\n\nSee the [Development Guide](https://github.com/kingabzpro/A-to-Z-MLOps/wiki/Development) for contribution workflow and coding standards.\n\n---\n\nMIT License - Created for the MLOps community\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkingabzpro%2Fa-to-z-mlops","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkingabzpro%2Fa-to-z-mlops","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkingabzpro%2Fa-to-z-mlops/lists"}