Introduction to Docker for Model Deployment
When machine learning models move from development to production, differences
in environments often cause deployment failures. Docker solves this problem
by packaging applications, dependencies, and configurations into portable
containers.
Docker has become a standard tool for deploying machine learning models
reliably across development, testing, and production environments.
⭐ What is Docker?
Docker is a containerization platform that allows applications to run in
isolated environments called containers. Each container includes the
application code, libraries, and system dependencies.
📌 Why Docker is Important for ML Deployment
- Eliminates “it works on my machine” problems
- Ensures consistent environments
- Easy scalability and portability
- Faster deployment cycles
⭐ Docker Architecture
- Docker Engine: Core runtime environment
- Docker Image: Blueprint for containers
- Docker Container: Running instance of an image
- Docker Registry: Stores Docker images (Docker Hub)
📌 Docker Image vs Docker Container
- Image: Static template with application and dependencies
- Container: Running instance of an image
⭐ Creating a Dockerfile for ML API
A Dockerfile defines how a Docker image is built. It includes instructions
to install dependencies, copy code, and run the application.
FROM python:3.10
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8000"]
📌 Building and Running Docker Containers
# Build Docker image
docker build -t ml-api .
# Run Docker container
docker run -p 8000:8000 ml-api
📌 Dockerizing a Machine Learning Model
- Serialize trained model
- Create API using Flask or FastAPI
- Define dependencies in requirements.txt
- Build and run Docker container
📌 Benefits of Docker for ML Systems
- Easy deployment across environments
- Supports CI/CD pipelines
- Works seamlessly with cloud platforms
- Improves scalability and maintainability
📌 Real-Life Applications
- Deploying ML APIs in production
- Microservices-based architectures
- Cloud-native machine learning systems
- DevOps and MLOps workflows
📌 Project Title
Containerized Machine Learning Model Deployment Using Docker
📌 Project Description
In this project, you will containerize a machine learning API using Docker.
The project demonstrates how to package a trained model, API code, and
dependencies into a Docker image and deploy it consistently across systems.
📌 Summary
Docker is a critical tool for deploying machine learning models reliably.
By using containers, ML applications become portable, scalable, and easier
to manage in production environments. This chapter prepares you for cloud-based
deployment on platforms like AWS, GCP, and Azure.
