Intermediate 20 min read
Model Deployment with Docker
Containerize and deploy your ML models with Docker.
By James WilsonUpdated April 5, 2026
Docker provides a consistent environment for deploying ML models. This guide covers containerizing your model from development to production.
Why Docker for ML?
Benefits include:
- •Consistent environments across dev/prod
- •Easy dependency management
- •Scalable deployments
- •Version control for infrastructure
Prerequisites
- Docker installed locally
- •A trained ML model
- •Basic command line knowledge
- •Python requirements file
Creating a Dockerfile
Start with a base Python image: ```dockerfile FROM python:3.9-slim WORKDIR /app COPY requirements.txt . RUN pip install -r requirements.txt COPY . . CMD ['python', 'serve.py'] ```
Model Serving Options
Choose your serving framework:
- •Flask/FastAPI: Simple REST APIs
- •TensorFlow Serving: Optimized for TF models
- •Triton: Multi-framework support
- •TorchServe: PyTorch models
Building the Image
Build your Docker image: ```bash docker build -t my-model:v1 . ```
Running Locally
Test locally before deployment: ```bash docker run -p 8080:8080 my-model:v1 ```
Optimization Tips
- Use multi-stage builds
- •Minimize image size
- •Use .dockerignore
- •Consider GPU base images for inference
Production Deployment
Deploy to cloud platforms:
- •AWS ECS/EKS
- •Google Cloud Run
- •Azure Container Instances
- •Kubernetes clusters