Hey everyone! 👋
Today, I’m excited to share how I containerized a simple Machine Learning project using Docker. If you’re new to Docker or Machine Learning, don't worry — I’ll explain everything step-by-step!
When building ML projects, the environment (Python version, libraries, dependencies) matters a lot.
Docker helps you create a consistent environment that works everywhere — on your machine, on the cloud, and even for your teammates.
Benefits:
No "it works on my machine" problem.
Easy to deploy anywhere.
Cleaner project structure
🛠️ Setting Up the Project
Here’s a simple project structure:
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machine_learning/
│
├── hello_world.py
└── Dockerfile
📦 Writing the Dockerfile
To containerize this project, I created a Dockerfile:
🛠️ Building the Docker Image
Inside the project folder, I opened the terminal and ran:
Start the Docker:
🚀 Running the Container
Now, I can run my Machine Learning project inside the container:
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