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https://github.com/bjornmelin/ml-production-engineering
⚙️ End-to-end ML deployment solutions. Focused on model serving, multi-GPU optimization, and production-grade system implementation. 🎯
https://github.com/bjornmelin/ml-production-engineering
cuda deployment docker fastapi gpu-computing kubernetes mlops production
Last synced: 21 days ago
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⚙️ End-to-end ML deployment solutions. Focused on model serving, multi-GPU optimization, and production-grade system implementation. 🎯
- Host: GitHub
- URL: https://github.com/bjornmelin/ml-production-engineering
- Owner: BjornMelin
- License: mit
- Created: 2025-01-24T14:43:01.000Z (22 days ago)
- Default Branch: main
- Last Pushed: 2025-01-24T14:45:40.000Z (22 days ago)
- Last Synced: 2025-01-24T15:32:17.748Z (22 days ago)
- Topics: cuda, deployment, docker, fastapi, gpu-computing, kubernetes, mlops, production
- Homepage:
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# ML Production Engineering ⚙️
[](https://www.python.org/downloads/)
[](https://onnx.ai)
[](https://www.docker.com/)
[](https://developer.nvidia.com/cuda-toolkit)
[](LICENSE)
> End-to-end machine learning deployment and optimization solutions. Focused on model serving, optimization, and production-grade ML system implementation.
[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)
- [Deployment](#deployment)
- [Optimization](#optimization)
- [Benchmarks](#benchmarks)
- [Contributing](#contributing)
- [Versioning](#versioning)
- [Authors](#authors)
- [Citation](#citation)
- [License](#license)
- [Acknowledgments](#acknowledgments)
## ✨ Features
- Model optimization and quantization
- Containerized deployment
- Multi-GPU serving strategies
- A/B testing infrastructure
- Monitoring and observability
## 📁 Project Structure
```mermaid
graph TD
A[ml-production-engineering] --> B[serving]
A --> C[optimization]
A --> D[monitoring]
A --> E[deployment]
B --> F[endpoints]
B --> G[scaling]
C --> H[quantization]
C --> I[compression]
D --> J[metrics]
D --> K[alerts]
E --> L[containers]
E --> M[orchestration]
```
Click to expand full directory structure
```plaintext
ml-production-engineering/
├── serving/ # Model serving components
│ ├── endpoints/ # API endpoints
│ └── scaling/ # Scaling utilities
├── optimization/ # Model optimization
│ ├── quantization/ # Model quantization
│ └── compression/ # Model compression
├── monitoring/ # Monitoring tools
├── deployment/ # Deployment configs
├── tests/ # Unit tests
└── README.md # Documentation
```
## 🔧 Prerequisites
- Python 3.8+
- CUDA 11.8+
- Docker 24.0+
- Kubernetes 1.24+
- NVIDIA GPU (Compute Capability 6.0+)
## 📦 Installation
```bash
# Clone repository
git clone https://github.com/BjornMelin/ml-production-engineering.git
cd ml-production-engineering
# Create environment
python -m venv venv
source venv/bin/activate
# Install dependencies
pip install -r requirements.txt
```
## 🚀 Quick Start
```python
from mlprod import optimization, serving
# Optimize model
optimized_model = optimization.quantize_model(
model,
backend="tensorrt",
precision="fp16"
)
# Deploy model
deployment = serving.ModelDeployment(
model=optimized_model,
scaling_config={
"min_replicas": 2,
"max_replicas": 10
}
)
# Launch service
deployment.deploy()
```
## 📚 Documentation
### Deployment Patterns
| Pattern | Use Case | Scalability | Complexity |
|---------|----------|-------------|------------|
| REST API | Online Inference | High | Low |
| Batch Processing | Offline Inference | Very High | Medium |
| Streaming | Real-time Processing | High | High |
### Optimization Techniques
- Model quantization (INT8/FP16)
- Model pruning and distillation
- TensorRT optimization
- Batch inference optimization
### Benchmarks
Production performance metrics:
| Metric | Value | Environment | Load |
|--------|-------|-------------|------|
| Latency P95 | 25ms | K8s + GPU | 1000 RPS |
| Throughput | 5000 RPS | Auto-scaled | Peak |
| Memory Usage | 4GB/pod | Optimized | Steady |
## 🤝 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/ml-production-engineering/tags).
## ✍️ Authors
**Bjorn Melin**
- GitHub: [@BjornMelin](https://github.com/BjornMelin)
- LinkedIn: [Bjorn Melin](https://linkedin.com/in/bjorn-melin)
## 📝 Citation
```bibtex
@misc{melin2024mlproductionengineering,
author = {Melin, Bjorn},
title = {ML Production Engineering: Production-Grade ML Systems},
year = {2024},
publisher = {GitHub},
url = {https://github.com/BjornMelin/ml-production-engineering}
}
```
## 📄 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## 🙏 Acknowledgments
- ONNX community
- TensorRT team
- Kubernetes community
---
Made with ⚙️ and ❤️ by Bjorn Melin