{"id":25257584,"url":"https://github.com/bjornmelin/mlops-toolkit","last_synced_at":"2025-08-16T05:36:45.221Z","repository":{"id":274052575,"uuid":"921750883","full_name":"BjornMelin/mlops-toolkit","owner":"BjornMelin","description":"🛠️ Enterprise ML infrastructure and deployment tools. 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Comprehensive suite of tools and implementations for managing ML lifecycle, experiments, and deployments.\n\n[Features](#features) • [Installation](#installation) • [Quick Start](#quick-start) • [Documentation](#documentation) • [Contributing](#contributing)\n\n## 📑 Table of Contents\n- [Features](#features)\n- [Project Structure](#project-structure)\n- [Prerequisites](#prerequisites)\n- [Installation](#installation)\n- [Quick Start](#quick-start)\n- [Documentation](#documentation)\n  - [Components](#components)\n  - [Integration](#integration)\n  - [Benchmarks](#benchmarks)\n- [Contributing](#contributing)\n- [Versioning](#versioning)\n- [Authors](#authors)\n- [Citation](#citation)\n- [License](#license)\n- [Acknowledgments](#acknowledgments)\n\n## ✨ Features\n- Automated ML pipelines\n- Experiment tracking and versioning\n- Model registry and deployment\n- A/B testing framework\n- Monitoring and alerting\n- Feature store implementation\n\n## 📁 Project Structure\n\n```mermaid\ngraph TD\n    A[mlops-toolkit] --\u003e B[pipelines]\n    A --\u003e C[monitoring]\n    A --\u003e D[registry]\n    A --\u003e E[deployment]\n    B --\u003e F[training]\n    B --\u003e G[evaluation]\n    C --\u003e H[metrics]\n    C --\u003e I[alerts]\n    D --\u003e J[models]\n    D --\u003e K[artifacts]\n    E --\u003e L[kubernetes]\n    E --\u003e M[serving]\n```\n\n\u003cdetails\u003e\n\u003csummary\u003eClick to expand full directory structure\u003c/summary\u003e\n\n```plaintext\nmlops-toolkit/\n├── pipelines/         # ML pipelines\n│   ├── training/     # Training pipelines\n│   └── evaluation/   # Evaluation pipelines\n├── monitoring/       # Monitoring suite\n│   ├── metrics/     # Metrics collection\n│   └── alerts/      # Alerting system\n├── registry/         # Model registry\n├── deployment/       # Deployment tools\n├── tests/           # Unit tests\n└── README.md        # Documentation\n```\n\u003c/details\u003e\n\n## 🔧 Prerequisites\n- Python 3.8+\n- MLflow 2.9+\n- DVC 3.30+\n- Kubernetes 1.24+\n- PostgreSQL 13+\n\n## 📦 Installation\n\n```bash\n# Clone repository\ngit clone https://github.com/BjornMelin/mlops-toolkit.git\ncd mlops-toolkit\n\n# Create environment\npython -m venv venv\nsource venv/bin/activate\n\n# Install dependencies\npip install -r requirements.txt\n\n# Initialize infrastructure\nmake init-infrastructure\n```\n\n## 🚀 Quick Start\n\n```python\nfrom mlops_toolkit import pipeline, monitoring\n\n# Create training pipeline\npipeline = pipeline.MLPipeline(\n    name=\"training-pipeline\",\n    steps=[\n        pipeline.DataPrep(),\n        pipeline.Training(),\n        pipeline.Evaluation()\n    ]\n)\n\n# Configure monitoring\nmonitoring = monitoring.ModelMonitoring(\n    metrics=[\"accuracy\", \"latency\"],\n    alerts_config={\n        \"accuracy_threshold\": 0.95,\n        \"latency_p95_ms\": 100\n    }\n)\n\n# Run pipeline with monitoring\npipeline.run(monitoring=monitoring)\n```\n\n## 📚 Documentation\n\n### Components\n\n| Component | Purpose | Integration Points | Scalability |\n|-----------|---------|-------------------|-------------|\n| Model Registry | Version Control | Git, DVC | High |\n| Feature Store | Feature Management | PostgreSQL, Redis | Very High |\n| Monitoring | Performance Tracking | Prometheus, Grafana | High |\n| Pipeline Orchestration | Workflow Management | Airflow, Kubernetes | High |\n\n### Integration\n- CI/CD pipeline integration\n- Kubernetes deployment\n- Cloud provider support\n- Monitoring stack setup\n\n### Benchmarks\nSystem performance metrics:\n\n| Operation | Scale | Latency | Throughput |\n|-----------|-------|---------|------------|\n| Model Registration | 100 models/day | 2s | 50 ops/sec |\n| Feature Serving | 10TB dataset | 20ms | 10k req/sec |\n| Pipeline Execution | 50 concurrent | 5min | 20 jobs/min |\n\n## 🤝 Contributing\n- [Contributing Guidelines](CONTRIBUTING.md)\n- [Code of Conduct](CODE_OF_CONDUCT.md)\n- [Development Guide](DEVELOPMENT.md)\n\n## 📌 Versioning\nWe use [SemVer](http://semver.org/) for versioning. For available versions, see the [tags on this repository](https://github.com/BjornMelin/mlops-toolkit/tags).\n\n## ✍️ Authors\n**Bjorn Melin**\n- GitHub: [@BjornMelin](https://github.com/BjornMelin)\n- LinkedIn: [Bjorn Melin](https://linkedin.com/in/bjorn-melin)\n\n## 📝 Citation\n```bibtex\n@misc{melin2024mlopstoolkit,\n  author = {Melin, Bjorn},\n  title = {MLOps Toolkit: Enterprise ML Infrastructure Tools},\n  year = {2024},\n  publisher = {GitHub},\n  url = {https://github.com/BjornMelin/mlops-toolkit}\n}\n```\n\n## 📄 License\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n## 🙏 Acknowledgments\n- MLflow community\n- DVC team\n- Kubernetes contributors\n- Open source MLOps community\n\n---\nMade with 🛠️ and ❤️ by Bjorn Melin\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbjornmelin%2Fmlops-toolkit","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbjornmelin%2Fmlops-toolkit","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbjornmelin%2Fmlops-toolkit/lists"}