Docker and Kubernetes for ML Model Deployment

# 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 ```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.