Docker and Kubernetes for ML Model Deployment
Complete guide to containerizing machine learning models and deploying them with Kubernetes for scalable, production-ready services.
Docker and Kubernetes for ML Model Deployment
Deploying machine learning models to production requires robust infrastructure. Docker and Kubernetes provide the tools for scalable, maintainable deployments.
Why Containers for ML?
Reproducibility
Containers ensure your model runs the same everywhere - development, testing, production.
Dependency Management
No more "works on my machine" issues. All dependencies are packaged together.
Scalability
Easy horizontal scaling to handle varying loads.
Docker Basics
Creating a Dockerfile
FROM python:3.9-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
EXPOSE 8000
CMD ["uvicorn", "main:app", "--host", "0.0.0.0"]
Best Practices
- Use slim base images
- Multi-stage builds for smaller images
- Don't run as root
- Use .dockerignore
Kubernetes Deployment
Deployment Configuration
Create a Deployment for your model server with resource limits, replicas, and health checks.
Service Exposure
Use Services to expose your deployment:
- ClusterIP for internal access
- LoadBalancer for external access
- Ingress for HTTP routing
Scaling
Horizontal Pod Autoscaler for automatic scaling based on CPU/memory usage or custom metrics.
ML-Specific Considerations
Model Versioning
Use model registries and include version in image tags.
GPU Support
NVIDIA device plugin for Kubernetes enables GPU access.
Model Loading
Load models at startup, not per-request.
Batch Processing
Implement batch inference for throughput.
Monitoring
Metrics
Prometheus for collecting metrics:
- Request latency
- Prediction counts
- Model accuracy
- Resource usage
Logging
Structured logging with ELK stack or similar.
Alerting
Set up alerts for model drift, high latency, errors.
CI/CD Pipeline
Automate the entire process:
- Code push triggers build
- Run tests
- Build Docker image
- Push to registry
- Deploy to Kubernetes
Conclusion
Docker and Kubernetes provide the foundation for reliable ML deployments. Start simple and add complexity as needed.
