Documentation Best Practices

Documentation Best Practices#

(a.k.a. “Writing Stuff So Future You Doesn’t Hate Past You”)

Let’s be honest — documentation is the broccoli of software development. Everyone knows it’s good for you, but most people would rather just… not. 🥦

Yet when your ML app goes into production and someone (maybe you, maybe an intern) has to figure out why train_model_v3_final_NEWER.py keeps emailing customers cat GIFs — you’ll wish you’d written something down.

So let’s learn how to document like a pro, with style, structure, and just enough sarcasm to keep it fun.

💬 Why Documentation Matters (Especially for ML & Business Apps)#

Because your future self has the memory of a goldfish. Because your teammates don’t read minds (or your 2,000-line notebook). Because when your model predicts negative prices, you’ll need evidence that you warned everyone about it. 😅

“Code tells you how. Documentation tells you why.”

Good documentation doesn’t just describe your functions — it tells the story of your system.

📖 1. Layers of Documentation#

(Yes, documentation also has architecture — surprise!)

Think of it as a layered cake 🍰 — each layer serving a different audience.

Layer	Audience	Content
README.md	New developers, managers	Project overview, setup, and usage
Code docstrings	Fellow engineers	Detailed how-tos for functions/classes
API docs	Users or integrations	Endpoints, request/response formats
Experiment tracking	Data scientists	Model configs, metrics, and results
Business reports	Stakeholders	Insights, KPIs, ROI, TL;DR

When your project scales, documentation is the only thing keeping it from becoming a myth.

🧱 2. The README: Your Project’s Tinder Bio#

This is the first thing people see. Make it charming and informative.

Bad README:

This project does ML.

Better README:

# Dynamic Pricing Optimization System

Predicts product prices based on historical demand and competitor data.

## Features
- Automated data ingestion from CSV and APIs
- XGBoost and Prophet model integration
- REST API for real-time predictions
- Docker + CI/CD ready for deployment

## Setup

pip install -r requirements.txt python app.py

## Author
The brave soul who documented this: @you

Your README should:

Explain what it does
Show how to run it
Include screenshots or sample outputs
Be understandable even for non-ML people (“It predicts prices” > “It performs nonlinear regression with gradient boosting trees”)

🧩 3. Code Documentation: The Sacred Art of the Docstring#

Docstrings are your code’s whisper to the world. They’re how your functions politely explain themselves instead of silently judging users.

Example:#

def forecast_sales(data, model, periods=30):
    """
    Predicts future sales using the specified model.

    Args:
        data (pd.DataFrame): Historical sales data.
        model (sklearn.BaseEstimator): Trained forecasting model.
        periods (int): Number of future days to forecast.

    Returns:
        pd.DataFrame: Predicted sales for the given period.
    """
    pass

This is how you write docstrings that actually help: ✅ Explains purpose ✅ Lists arguments and types ✅ Describes return values ✅ Adds default values where relevant

A good docstring is like a good dating profile: clear, honest, and leaves no confusion about intentions.

🧠 4. Auto-Documentation Tools: Because Copy-Pasting Hurts#

Use tools to generate documentation automatically from your codebase. This way, your docs stay up-to-date — even when you forget to.

🧰 Popular Tools#

Sphinx → Turns docstrings into beautiful HTML docs
MkDocs → Lightweight and Markdown-based
pdoc / pdoc3 → Auto-generates docs with one command
Jupyter nbconvert → Turns notebooks into reports

Example:

pip install pdoc
pdoc --html my_project

Now you have professional-looking docs. Show them off like a portfolio piece on LinkedIn.

🧾 5. API Documentation: Don’t Make Users Guess#

When you build ML-powered APIs, good documentation means fewer support calls like:

“Hey, what does /predict do? It returned 403 and a meme.”

Use OpenAPI (Swagger) or FastAPI’s built-in docs to make your endpoints self-explanatory.

Example with FastAPI:#

from fastapi import FastAPI
app = FastAPI(title="Sales Prediction API", version="1.0")

@app.post("/predict")
def predict_sales(item: dict):
    """
    Predict sales based on input features.
    - **item**: JSON with 'price', 'region', and 'marketing_spend'.
    """
    return {"sales": 1245.7}

Then visit /docs → boom 💥 instant interactive API docs. Stakeholders love it, and devs stop guessing payload formats.

🧮 6. ML Experiment Documentation#

Machine Learning projects evolve fast — sometimes faster than your memory. Document your experiments, hyperparameters, and results like a research scientist.

Use tools like:#

MLflow 🧪 — Track experiments, models, and metrics
Weights & Biases 🎯 — Visual dashboards and reports
Neptune.ai, Comet.ml, etc.

Log your:

Data version
Model parameters
Evaluation metrics
Environment (Python, OS, library versions)

Because nothing says “I’m a professional” like being able to reproduce your model from six months ago.

🗃️ 7. Business-Level Documentation#

You’ve built a brilliant ML engine, but if the business team doesn’t understand it, they’ll think it’s witchcraft. 🧙‍♂️

Translate your technical brilliance into business terms:

“Mean Absolute Error decreased by 10%” → “Forecasting accuracy improved by 10%, reducing overstock risk.”
“Hyperparameter tuning completed” → “We made the model less of a drama queen.”

Use visual reports (Tableau, Streamlit, or Markdown dashboards) to tell stories, not just numbers.

🧠 8. Versioning and Change Logs#

Your docs should evolve with your code — not sit in 2021 forever.

Add a CHANGELOG.md with updates like:

## v2.1.0 (Oct 2025)
- Added new feature: Real-time sales forecasting
- Replaced Prophet with LSTM
- Fixed bug: Model occasionally predicted negative demand (oops)

Because “fixed a few bugs” isn’t documentation — it’s emotional avoidance. 😆

🧩 9. Example: Well-Documented ML Project Structure#

ml_pricing_system/
│
├── README.md
├── CHANGELOG.md
├── docs/
│   ├── api_reference/
│   ├── architecture_diagram.png
│   └── model_report.md
│
├── src/
│   ├── data/
│   ├── models/
│   ├── utils/
│   └── api/
│
└── notebooks/
    ├── 01_exploration.ipynb
    ├── 02_training.ipynb
    └── 03_evaluation.ipynb

Now your project not only works — it teaches itself to whoever joins next. Even future you.

💬 Final Thoughts#

Documentation is your system’s memory — without it, even the best algorithm is just a mysterious black box with imposter syndrome.

Good docs:

Save time
Build trust
Turn chaos into continuity

“Bad documentation is like bad handwriting — even the author can’t read it later.” ✍️

So document your brilliance now — before it becomes tomorrow’s archaeology project. 🏺

# Your code here