ML project structure in ML Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the main purpose of having a clear ML project structure?

A clear ML project structure helps organize code, data, and experiments so the project is easy to understand, reproduce, and maintain.

beginner

Name three common folders you might find in an ML project.

Common folders include data (for raw and processed data), src (source code), and models (saved trained models).

intermediate

Why should raw data be kept separate from processed data in an ML project?

Keeping raw data separate ensures the original data is never changed accidentally and allows easy reprocessing if needed.

beginner

What role does a README file play in an ML project?

A README file explains the project purpose, how to run the code, and any important details, helping others understand and use the project.

intermediate

How can version control help in managing an ML project?

Version control tracks changes in code and files, allowing you to go back to previous versions and collaborate safely with others.

Which folder typically contains the scripts to train and test ML models?

A data

B src

C models

D docs

The src folder usually holds source code like training and testing scripts.

Why is it important to separate raw data from processed data?

A To prevent accidental changes to original data

B To save disk space

C To speed up training

D To make code shorter

Separating raw data protects the original data from accidental edits.

What is the purpose of the 'models' folder in an ML project?

A Store raw data files

B Keep visualization images

C Write documentation

D Save trained model files

The models folder stores saved trained models for reuse or deployment.

Which file helps others understand how to use your ML project?

A README.md

B requirements.txt

C train.py

D data.csv

The README.md file explains project details and usage instructions.

How does version control benefit ML projects?

A Automatically improves model accuracy

B Stores large datasets

C Tracks changes and supports collaboration

D Runs code faster

Version control helps track code changes and allows multiple people to work together safely.

Describe the typical folder structure of an ML project and the purpose of each folder.

Think about where you keep data, code, and explanations.

Explain why organizing data and code properly is important in an ML project.

Consider what happens if files are mixed up or missing.

Practice

1. Which folder in an ML project should you use to store the original, untouched data files?

easy

A. Raw data folder

B. Processed data folder

C. Models folder

D. Scripts folder

Solution

1. Step 1: Understand the purpose of raw data folder

   The raw data folder is meant to keep the original data safe and unchanged.

2. Step 2: Differentiate from processed data folder

   The processed data folder contains cleaned or transformed data, not the original files.

3. Final Answer:

   Raw data folder -> Option A

4. Quick Check:

   Original data = Raw data folder [OK]

Hint: Original files go to raw data folder to keep them safe [OK]

Common Mistakes:

• Putting original data in processed folder
• Mixing models with data files
• Saving scripts in data folders

2. Which of the following is the correct way to organize code in an ML project?

easy

A. Put all code, notebooks, and models in one folder

B. Keep processed data and scripts mixed in one folder

C. Separate notebooks for exploration and scripts for training

D. Save models inside the raw data folder

Solution

1. Step 1: Identify the role of notebooks and scripts

   Notebooks are for exploring data and ideas; scripts are for running training and processing tasks.

2. Step 2: Understand folder separation

   Keeping notebooks and scripts separate helps keep the project organized and clear.

3. Final Answer:

   Separate notebooks for exploration and scripts for training -> Option C

4. Quick Check:

   Notebooks ≠ scripts folder [OK]

Hint: Use notebooks for exploring, scripts for running training [OK]

Common Mistakes:

• Mixing notebooks and scripts in one folder
• Saving models in data folders
• Putting all files in one folder

3. Given this ML project folder structure:

project/
├── data/
│   ├── raw/
│   └── processed/
├── notebooks/
├── scripts/
└── models/

Where should you save a trained model file after training?

medium

A. data/raw/

B. scripts/

C. notebooks/

D. models/

Solution

1. Step 1: Understand folder purposes

   The models folder is designed to store trained model files for reuse and deployment.

2. Step 2: Exclude other folders

   Data folders are for data files, notebooks for exploration, scripts for code execution, so models belong in models/.

3. Final Answer:

   models/ -> Option D

4. Quick Check:

   Trained model files go to models folder [OK]

Hint: Save trained models in the models folder [OK]

Common Mistakes:

• Saving models in data folders
• Putting models inside notebooks folder
• Confusing scripts folder with models

4. You find that your ML project has raw data and processed data mixed in the same folder. What is the best fix?

medium

A. Rename all files to indicate if they are raw or processed

B. Move raw data to a separate 'raw' folder and processed data to 'processed' folder

C. Delete the processed data and keep only raw data

D. Keep all data in one folder but add a README explaining the files

Solution

1. Step 1: Identify the problem

   Mixing raw and processed data risks overwriting or confusion about data versions.

2. Step 2: Organize data properly

   Separating raw and processed data into different folders keeps original data safe and processed data clear.

3. Final Answer:

   Move raw data to a separate 'raw' folder and processed data to 'processed' folder -> Option B

4. Quick Check:

   Separate raw and processed data folders [OK]

Hint: Keep raw and processed data in separate folders [OK]

Common Mistakes:

• Deleting processed data unnecessarily
• Only renaming files without moving
• Relying on README without folder separation

5. You want to share your ML project with a teammate. Which folder should you exclude to keep the project size small and avoid sharing large temporary files?

hard

A. outputs/

B. data/raw/

C. models/

D. notebooks/

Solution

1. Step 1: Identify large temporary files

   The outputs folder usually contains temporary results like logs, plots, or intermediate files that can be large and rebuilt.

2. Step 2: Decide what to share

   Models, raw data, and notebooks are important for reproducing work, so outputs can be excluded to save space.

3. Final Answer:

   outputs/ -> Option A

4. Quick Check:

   Exclude outputs folder to reduce project size [OK]

Hint: Exclude outputs folder to avoid sharing large temp files [OK]

Common Mistakes:

• Excluding raw data which is needed
• Removing models folder by mistake
• Not excluding any folder and sharing everything