Lesson 22: Deploying Python Applications

What Deployment Means

Deployment is the process of taking your Python application from development to a live environment where users can access it. This may involve servers, containers, cloud platforms, or simple distribution methods depending on the project type.

Deployment Options

• Local deployment — running the app on your own machine.
• Server deployment — hosting on a VPS or dedicated server.
• Cloud deployment — using platforms like AWS, Azure, or Google Cloud.
• Container deployment — packaging the app with Docker.
• Static hosting — for static sites generated by Python tools.

Preparing Your Application

Before deploying, ensure your project is production‑ready:

• Use a virtual environment.
• Freeze dependencies into requirements.txt.
• Organize your project structure cleanly.
• Include configuration files and environment variables.

Environment Variables

Use environment variables to store sensitive information:

export API_KEY="your_key_here"

Deploying a Web App with Flask

Example minimal Flask app:

from flask import Flask
app = Flask(__name__)

@app.get("/")
def home():
    return "Hello from Flask!"

Running in Production with Gunicorn

pip install gunicorn
gunicorn app:app

Using Docker for Deployment

Docker packages your app and environment into a portable container.

Dockerfile Example

FROM python:3.11
WORKDIR /app
COPY . .
RUN pip install -r requirements.txt
CMD ["python", "main.py"]

Building and Running

docker build -t myapp .
docker run -p 8000:8000 myapp

Deploying to the Cloud

Popular cloud platforms for Python apps:

• AWS — EC2, Lambda, Elastic Beanstalk
• Azure — App Service, Functions
• Google Cloud — Cloud Run, App Engine
• Railway / Render — simple hosting for small apps

Logging and Monitoring

Production apps need visibility:

• Log errors and events.
• Monitor performance and uptime.
• Use tools like Prometheus, Grafana, or cloud dashboards.

Security Considerations

• Never commit secrets to Git.
• Use HTTPS for all web traffic.
• Keep dependencies updated.
• Limit server permissions and access.

Next Steps

Now that you can deploy Python applications, you're ready to explore automation and workflows in Lesson 23: Introduction to Automation and Scripting.

← Back to Lesson Index