dvcrn 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:
- Title & Purpose
- Imports (standard → third-party → local)
- Configs (all constants at top)
- Datasets (load, validate, split)
- Analysis (EDA)
- Modeling (use
sklearn.pipeline.Pipeline) - 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_stateeverywhere
❌ 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