MLOps Prototyping CN

Structured Jupyter notebook prototyping with pipeline integrity

MLOps Prototyping 🔬

Create standardized, reproducible Jupyter notebooks.

Features

1. Notebook Structure Check ✅

Validate notebook follows best practices:

./scripts/check-notebook.sh notebook.ipynb

Checks for:

• H1 title
• Imports section
• Config/Constants
• Data loading
• Pipeline usage

2. Template 📝

Use this structure:

1. Title & Purpose
2. Imports (standard → third-party → local)
3. Configs (all constants at top)
4. Datasets (load, validate, split)
5. Analysis (EDA)
6. Modeling (use sklearn.pipeline.Pipeline)
7. Evaluations (metrics on test data)

Quick Start

# Check your notebook
./scripts/check-notebook.sh my-notebook.ipynb

# Follow structure in notebook
# Use Pipeline for all transforms
# Set RANDOM_STATE everywhere

Key Rules

✅ DO:

• Put all params in Config section
• Use sklearn.pipeline.Pipeline
• Split data BEFORE any transforms
• Set random_state everywhere

❌ DON'T:

• Magic numbers in code
• Manual transforms (use Pipeline)
• Fit on full dataset (data leakage)

Author

Converted from MLOps Coding Course

Changelog

v1.0.0 (2026-02-18)

• Initial OpenClaw conversion
• Added notebook checker