Bonus: Docker for Automation Environments – Containerize Your Workflows

🔗 Knowledge graph – Docker enhances every automation you've built

Day 1-4

Run prompts in containers

Day 4

OpenAI API in Docker

Day 8

Containerize qualifier

Day 9

Email sequences in containers

Day 10

Content engine container

Day 11

Support router container

Day 14

CRM sync in container

Day 16

Scraper in container

Day 18

Proposal generator container

Day 19

Chatbot backend container

Day 21

Portfolio system containerized

Shared link: Every automation you've built can be packaged into a Docker container for easy deployment, scaling, and portability. This bonus module shows you how to take your no-code and low-code skills to the next level with cloud-native tools.

🐳 What is Docker? A Beginner's Guide

📌 Containers = Lightweight VMs for your code

Docker is a platform that packages your application and all its dependencies into a standardized unit called a container. Containers run the same everywhere – your laptop, a server, or the cloud.

Why for automation? Your Python scripts, API integrations, and AI workflows often need specific versions of Python, libraries, and environment variables. Docker ensures they run identically everywhere.

Analogy: Think of Docker like shipping containers. Before containers, cargo was loaded loosely – messy and incompatible. Shipping containers standardized everything. Docker does the same for software – your automation runs the same way everywhere.

Your laptop

Python 3.9, custom libraries

Cloud server

Same Python, same libraries

Client's machine

Exactly the same environment

⚖️ Docker vs Virtual Machines

Virtual Machines

Each VM has its own OS
Heavy (GBs)
Slow to start (minutes)
High resource usage
Good for running different OSes

Docker Containers

Share host OS kernel
Lightweight (MBs)
Instant start (seconds)
Low resource usage
Perfect for microservices

⚙️ Installing Docker

Download Docker Desktop

Go to docker.com/products/docker-desktop

Choose your OS (Windows/Mac/Linux)

Install and launch

Follow installation wizard. On Windows, enable WSL2 if prompted.

Verify installation

$ docker --version Docker version 24.0.7, build afdd53b $ docker run hello-world Hello from Docker!

📄 Dockerfile – The Recipe for Your Container

A Dockerfile is a text file with instructions to build your container image.

# Example Dockerfile for a Python automation script
FROM python:3.9-slim
# Set working directory
WORKDIR /app
# Copy requirements first (for caching)
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
# Copy your automation code
COPY . .
# Set environment variables
ENV OPENAI_API_KEY=${OPENAI_API_KEY}
# Run your script
CMD ["python", "main.py"]
            

requirements.txt example:

openai==1.3.0
requests==2.31.0
pandas==2.1.0
python-dotenv==1.0.0
                

🏗️ Building and Running Containers

Build the image

$ docker build -t my-automation:1.0 .

-t tags the image with a name and version

Run the container

$ docker run --env-file .env my-automation:1.0

--env-file passes environment variables (API keys securely)

List running containers

$ docker ps CONTAINER ID IMAGE COMMAND CREATED STATUS abc123def456 my-automation:1.0 "python main.py" 2 minutes ago Up 2 minutes

Stop a container

$ docker stop abc123def456

📦 Case Study: Containerizing Day 8 Lead Qualifier

Project Structure

lead-qualifier/
├── Dockerfile
├── requirements.txt
├── .env (not in git)
├── main.py
├── qualifier.py
└── hubspot_client.py
                    

main.py (simplified)

import os from qualifier import qualify_lead from hubspot_client import create_contact def main(): lead_data = { "email": "[email protected]", "message": "I'm looking to buy a house under 500k, pre-approved" } result = qualify_lead(lead_data) create_contact(result) if __name__ == "__main__": main()

Benefits: This container can run on any server, schedule with cron, or deploy to AWS/GCP without worrying about Python versions or missing libraries.

🔄 Docker Compose – Orchestrating Multiple Containers

Your Day 21 real estate system might need: API container + database + scheduler. Docker Compose runs them together.

# docker-compose.yml
version: '3.8'
services:
  api:
    build: ./api
    ports:
      - "8000:8000"
    environment:
      - OPENAI_API_KEY=${OPENAI_API_KEY}
    depends_on:
      - db
  db:
    image: postgres:15
    environment:
      POSTGRES_PASSWORD: ${DB_PASSWORD}
    volumes:
      - postgres_data:/var/lib/postgresql/data
  scheduler:
    build: ./scheduler
    depends_on:
      - api
      - db
volumes:
  postgres_data:
            

$ docker-compose up -d # Starts all services in background

☁️ Deploying Containers to the Cloud

AWS ECS

Elastic Container Service – managed Docker

Google Cloud Run

Serverless containers, pay per request

Azure Container Instances

Simple container hosting

DigitalOcean

App Platform with container support

# Push to Docker Hub $ docker tag my-automation:1.0 username/my-automation:1.0 $ docker push username/my-automation:1.0 # Then deploy from any cloud provider

✅ Docker Best Practices for Automation

Small images

Use slim/base images (python:3.9-slim). Smaller = faster, cheaper.

.dockerignore

Exclude .env, .git, __pycache__ from image

Environment variables

Never hardcode secrets. Use .env files or secrets manager.

Layered caching

Copy requirements.txt first, then code. Leverages Docker cache.

Health checks

Add HEALTHCHECK to monitor long-running automations.

Logging

Write logs to stdout/stderr for Docker to collect.

8 hands-on Docker exercises

🐳 Exercise 1: Install Docker

Install Docker Desktop. Run hello-world container. Take a screenshot.

📄 Exercise 2: Write a Dockerfile

Take any Python script (e.g., Day 4 OpenAI test). Write a Dockerfile. Build and run.

📦 Exercise 3: Containerize Day 8

Package your Day 8 lead qualifier into a container. Pass API keys via .env.

🔄 Exercise 4: Docker Compose

Create a docker-compose.yml with a script + postgres database.

☁️ Exercise 5: Push to Docker Hub

Create Docker Hub account. Push one of your images.

📊 Exercise 6: Container logs

Run a container, generate logs, practice docker logs command.

⚡ Exercise 7: Optimize image size

Take a Dockerfile, reduce image size by 50% using slim images and multi-stage builds.

🚀 Exercise 8: Deploy to cloud

Deploy a container to Google Cloud Run or AWS ECS (free tier).

📄 Client Proposal – Containerized Automation Deployment

🐳 Containerized Automation System – Proposal

What I'll deliver:

✅ Your automation packaged in Docker containers
✅ Environment configuration (API keys, secrets) securely managed
✅ Docker Compose for multi-service setups
✅ Deployment to cloud (AWS/GCP/Azure) with monitoring
✅ Automated backups and logging
✅ Scaling configuration for high volume

Benefits:

Run anywhere – laptop, server, cloud
No more "it works on my machine"
Easy updates and rollbacks
Professional, enterprise-ready deployment

Investment: $1,500 setup + $200/mo hosting & maintenance

📚 Resources

Bonus Module: You've mastered Docker for automation

✔ Understood containers vs VMs
✔ Installed Docker and ran first container
✔ Wrote Dockerfiles for automation scripts
✔ Containerized Day 8 lead qualifier
✔ Used Docker Compose for multi-container systems
✔ Learned cloud deployment options
✔ 8 hands-on exercises
✔ Ready to deploy automations professionally

From automation to cloud-native in one bonus module