Summary  
This chapter explains how to define a Dockerfile to containerize a Python application by selecting a base image, installing dependencies, copying code, exposing ports, and setting the startup command to create a portable container image.

General domain of usage  
MLOps (deploying machine learning model services)

In MLOps, **Docker** plays a crucial role by allowing you to package your application, its dependencies, and even your trained machine learning models into a single, portable container image. This image can be run on any machine that supports Docker, ensuring the environment remains consistent from your local development laptop to a production server or cloud environment. By eliminating "works on my machine" problems, Docker helps you deliver reliable, reproducible deployments for your FastAPI-based model services.

Containerization with **Docker** makes it much easier to scale your machine learning services horizontally and deploy them in cloud or on-premise infrastructure. You can spin up multiple identical containers to handle increased load, or quickly move your service between different environments without worrying about dependency conflicts.

Note

```
# Start from the official Python base image
FROM python:3.12.4-slim

# Set the working directory in the container
WORKDIR /app

# Copy the requirements file and install dependencies
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Copy the FastAPI app and model files into the container
COPY . .

# Expose the port FastAPI will run on
EXPOSE 8000

# Command to run the FastAPI app using uvicorn
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]
```

Why is Docker important in the ML model deployment process?

Learn how to deploy machine learning models using Docker, FastAPI, and MLflow. This focused course covers creating API endpoints for inference, containerizing models, and managing the model lifecycle for production-ready solutions.

Containerizing with Docker