CDI Practical User Guides
I PREFACE
👋 Welcome to the CDI Model Deployment Guide
II EDA
1
How do you read the dataset from the
data/
folder before deployment?
1.1
Explanation
1.2
Python Code
1.3
R Code
2
How do you train and save multiple models for deployment?
2.1
Explanation
2.2
Python Code
2.3
R Code
3
How do you evaluate models before deployment?
3.1
Explanation
3.2
Python Code
3.3
R Code
4
How do you serve saved models as prediction endpoints using FastAPI?
4.1
Explanation
4.2
Python Code (Define FastAPI App)
4.3
R Code
5
How do you run and test your FastAPI app using Uvicorn and Swagger UI?
5.1
Explanation
5.2
Run Command (Terminal)
5.3
Output (Sample)
5.4
Test in Your Browser
5.5
Example JSON Input
5.6
Troubleshooting
6
How do you visualize model evaluation results from CSV?
6.1
Explanation
6.2
Python Code
6.3
R Code
7
How do you get started with Docker before containerizing your model?
7.1
Explanation
7.2
Step 1: Install Docker Desktop
7.3
Step 2: Start Docker Desktop
7.4
Step 3: Confirm Docker is Active
7.5
Step 4: Write Your First Dockerfile
7.6
Step 5: Build and Run Your First Container
7.7
Step 6: Log in to Docker Hub
7.8
Step 7: Tag and Push Your Image
7.9
Step 8: Scan Your Image with Docker Scout (Optional)
7.10
Example Log Insights
8
How do you containerize your model API using Docker for reproducible deployment?
8.1
Explanation
8.2
Project Structure
8.3
Dockerfile
8.4
Libraries in requirements.txt (Minimum Needed)
8.5
Build the Docker Image
8.6
Run the Docker Container
8.7
Optional: .dockerignore
9
How do you tag and push your model image to Docker Hub?
9.1
📘 Explanation
9.2
Log into Docker Hub
9.3
Tag the image using your Docker Hub username
9.4
Push the image to Docker Hub
10
How do you pull and run your model API image from Docker Hub?
10.1
Explanation
10.2
Bash Code
10.2.1
Pull the image from Docker Hub
10.3
Run the Docker container
10.4
Test the running API
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Model Deployment Q&A Guide
Model Deployment Q&A Guide
Last updated: July 18, 2025
