notebook-ml-architect

To audit ML notebooks for data leakage, you analyze the notebook structure to detect anti-patterns and reproducibility issues, checking data preprocessing and model training steps for improper data flow.

You convert Jupyter notebooks to Python scripts by applying a conversion operation that transforms the notebook cells into structured, modular Python code, enabling better reproducibility and pipeline execution.

The best way to add reproducibility instrumentation to ML workflows is by generating templates that enforce seeding, environment capture, and logging, ensuring consistent experiment tracking across EDA and model training.

Generating ML experiment templates and executing refactored workflows relies on dependencies like papermill and nbclient to parameterize and run notebooks programmatically, ensuring structured and automated pipeline execution.

You can generate templates for EDA, classification, and other ML workflows by applying a templating operation to your existing notebooks, producing standardized structures for data preprocessing and model training.

You should audit ipynb files before production to identify data leakage, modularization gaps, and reproducibility issues, ensuring the notebook is structured as a reliable pipeline rather than an exploratory script.