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

1. Use slim base images
2. Multi-stage builds for smaller images
3. Don't run as root
4. 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:

1. Code push triggers build
2. Run tests
3. Build Docker image
4. Push to registry
5. Deploy to Kubernetes

Conclusion

Docker and Kubernetes provide the foundation for reliable ML deployments. Start simple and add complexity as needed.