> For the complete documentation index, see [llms.txt](https://lwang010.gitbook.io/longw/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://lwang010.gitbook.io/longw/mlops/chap-2.-tools/1.4-ml-related.md).

# 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
```