mlops-prototyping-cn

# MLOps Prototyping 🔬 Create standardized, reproducible Jupyter notebooks. ## Features ### 1. Notebook Structure Check ✅ Validate notebook follows best practices: ```bash ./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 ```bash # 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](https://github.com/MLOps-Courses/mlops-coding-skills) ## Changelog ### v1.0.0 (2026-02-18) - Initial OpenClaw conversion - Added notebook checker