1.7 ML-related recommends

in case you missed one or two that could be relevant to your project?

1. JAX: creating and training models

2. jupytext: Jupyter notebooks as readable, editable documents

3. Streamlit: creating ML apps

4. excalidraw: virtual whiteboard for hand-drawn sketches

5. Facets: dataset visualization

6. D3: turns data into awesome interactive graphs

7. SHAP: model interpretability

8. Prefect: workflow orchestration

9. PyTorch Lightning: Keras for PyTorch

10. Prometheus: monitoring

11. anaconda: prefer it over pip because it handles the packages version conflict much better.

12. jupyter-notebook / Deepnote (https://deepnote.com/)

    1. follow the order of cell and don't just run it randomly

    2. add comments and docs because you'll forget what the code is about after a week or longer for sure

    3. use different kernels when there are multiple env involved. (use it as virtual env)

    4. Update jupyter-notebook theme

pip install jupyterthemes
jt -t solarizedd -f fira -fs 115