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Streamlit[0] was created specifically to create dashboards for ML/data science groups, and I've found it pretty useful. I've used it for research (model inspection and development), as well as teaching and it's been pretty useful for that.
[0] - https://streamlit.io/
Re your HTML point, I've been hacking on a framework which lets you programmatically create and share data reports in Python which you might find helpful (https://github.com/datapane/datapane). It supports Plotly, Pandas, Altair, Folium, MPL, etc., and provides some neat layout components like pages, selects, columns, and dropdowns.
If you need anything or want some help, feel free to make an issue or ping me on leo [-at-] datapane.com
> Rendering/Publishing/Conversion https://github.com/markusschanta/awesome-jupyter#renderingpu... :
> ContainDS Dashboards - JupyterHub extension to host authenticated scripts or notebooks in any framework (Voilà, Streamlit, Plotly Dash etc)
Streamlit lists Bokeh, Jupyter Voila , Panel, and Plotly Dash as Alternative dashboard approaches:
From the readme I can't tell what powers the cited REST API functionality. From setup.py > mercury/requirements.txt it's DRF: Django REST Framework. https://github.com/mljar/mercury/blob/main/mercury/requireme...
E.g. BentoML is built on FastAPI which is async (sanic) and built by the DRF people, but FastAPI doesn't have the plethora of packages with tests supported by the Django community and DSF Django Software Foundation.