https://github.com/bjornmelin/mlops-toolkit
🛠️ Enterprise ML infrastructure and deployment tools. Comprehensive suite for ML lifecycle management with focus on GPU cluster optimization. 📊
https://github.com/bjornmelin/mlops-toolkit
deployment dvc gpu-cluster kubeflow mlflow mlops model-registry monitoring python
Last synced: about 2 months ago
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🛠️ Enterprise ML infrastructure and deployment tools. Comprehensive suite for ML lifecycle management with focus on GPU cluster optimization. 📊
- Host: GitHub
- URL: https://github.com/bjornmelin/mlops-toolkit
- Owner: BjornMelin
- License: mit
- Created: 2025-01-24T14:46:57.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-01-24T15:02:21.000Z (9 months ago)
- Last Synced: 2025-04-06T01:13:25.349Z (6 months ago)
- Topics: deployment, dvc, gpu-cluster, kubeflow, mlflow, mlops, model-registry, monitoring, python
- Homepage:
- Size: 5.86 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# MLOps Toolkit 🛠️
[](https://www.python.org/downloads/)
[](https://mlflow.org/)
[](https://dvc.org/)
[](https://kubernetes.io/)
[](LICENSE)
> Enterprise-grade ML infrastructure and deployment tools. Comprehensive suite of tools and implementations for managing ML lifecycle, experiments, and deployments.
[Features](#features) • [Installation](#installation) • [Quick Start](#quick-start) • [Documentation](#documentation) • [Contributing](#contributing)
## 📑 Table of Contents
- [Features](#features)
- [Project Structure](#project-structure)
- [Prerequisites](#prerequisites)
- [Installation](#installation)
- [Quick Start](#quick-start)
- [Documentation](#documentation)
- [Components](#components)
- [Integration](#integration)
- [Benchmarks](#benchmarks)
- [Contributing](#contributing)
- [Versioning](#versioning)
- [Authors](#authors)
- [Citation](#citation)
- [License](#license)
- [Acknowledgments](#acknowledgments)
## ✨ Features
- Automated ML pipelines
- Experiment tracking and versioning
- Model registry and deployment
- A/B testing framework
- Monitoring and alerting
- Feature store implementation
## 📁 Project Structure
```mermaid
graph TD
A[mlops-toolkit] --> B[pipelines]
A --> C[monitoring]
A --> D[registry]
A --> E[deployment]
B --> F[training]
B --> G[evaluation]
C --> H[metrics]
C --> I[alerts]
D --> J[models]
D --> K[artifacts]
E --> L[kubernetes]
E --> M[serving]
```
Click to expand full directory structure
```plaintext
mlops-toolkit/
├── pipelines/ # ML pipelines
│ ├── training/ # Training pipelines
│ └── evaluation/ # Evaluation pipelines
├── monitoring/ # Monitoring suite
│ ├── metrics/ # Metrics collection
│ └── alerts/ # Alerting system
├── registry/ # Model registry
├── deployment/ # Deployment tools
├── tests/ # Unit tests
└── README.md # Documentation
```
## 🔧 Prerequisites
- Python 3.8+
- MLflow 2.9+
- DVC 3.30+
- Kubernetes 1.24+
- PostgreSQL 13+
## 📦 Installation
```bash
# Clone repository
git clone https://github.com/BjornMelin/mlops-toolkit.git
cd mlops-toolkit
# Create environment
python -m venv venv
source venv/bin/activate
# Install dependencies
pip install -r requirements.txt
# Initialize infrastructure
make init-infrastructure
```
## 🚀 Quick Start
```python
from mlops_toolkit import pipeline, monitoring
# Create training pipeline
pipeline = pipeline.MLPipeline(
name="training-pipeline",
steps=[
pipeline.DataPrep(),
pipeline.Training(),
pipeline.Evaluation()
]
)
# Configure monitoring
monitoring = monitoring.ModelMonitoring(
metrics=["accuracy", "latency"],
alerts_config={
"accuracy_threshold": 0.95,
"latency_p95_ms": 100
}
)
# Run pipeline with monitoring
pipeline.run(monitoring=monitoring)
```
## 📚 Documentation
### Components
| Component | Purpose | Integration Points | Scalability |
|-----------|---------|-------------------|-------------|
| Model Registry | Version Control | Git, DVC | High |
| Feature Store | Feature Management | PostgreSQL, Redis | Very High |
| Monitoring | Performance Tracking | Prometheus, Grafana | High |
| Pipeline Orchestration | Workflow Management | Airflow, Kubernetes | High |
### Integration
- CI/CD pipeline integration
- Kubernetes deployment
- Cloud provider support
- Monitoring stack setup
### Benchmarks
System performance metrics:
| Operation | Scale | Latency | Throughput |
|-----------|-------|---------|------------|
| Model Registration | 100 models/day | 2s | 50 ops/sec |
| Feature Serving | 10TB dataset | 20ms | 10k req/sec |
| Pipeline Execution | 50 concurrent | 5min | 20 jobs/min |
## 🤝 Contributing
- [Contributing Guidelines](CONTRIBUTING.md)
- [Code of Conduct](CODE_OF_CONDUCT.md)
- [Development Guide](DEVELOPMENT.md)
## 📌 Versioning
We use [SemVer](http://semver.org/) for versioning. For available versions, see the [tags on this repository](https://github.com/BjornMelin/mlops-toolkit/tags).
## ✍️ Authors
**Bjorn Melin**
- GitHub: [@BjornMelin](https://github.com/BjornMelin)
- LinkedIn: [Bjorn Melin](https://linkedin.com/in/bjorn-melin)
## 📝 Citation
```bibtex
@misc{melin2024mlopstoolkit,
author = {Melin, Bjorn},
title = {MLOps Toolkit: Enterprise ML Infrastructure Tools},
year = {2024},
publisher = {GitHub},
url = {https://github.com/BjornMelin/mlops-toolkit}
}
```
## 📄 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## 🙏 Acknowledgments
- MLflow community
- DVC team
- Kubernetes contributors
- Open source MLOps community
---
Made with 🛠️ and ❤️ by Bjorn Melin