Good to see this project on the front page! We are using Vega (specifically Vega-Lite [1] as an engine and templates spec for data-science plots / visualizations in DVC (e.g. how it looks like in VSCode extension [2]).
It allowed us to have:
- same engine in CLI (can generate HTML and open in browser), VSCode extensions, SaaS
- have a way to describe plot visualization / representation as a declarative spec that can be then used in all those products (plot spec). We were exploring plotly and AFAIU there was no easy way to do the same
- it's quite comprehensive and community is responsive, the project is maintained
To name a few downsides from our experience:
- DSL is quite complicated. It requires some time to master it. It hurts the adoption. In our case I don't see that many users doing custom plots / templates - majority is using pre-baked built-in stuff or use Python and export as SVG.
- In our case some features were missing (and are still missing) - exponential average - that is most commonly used to smooth ML training curves.
It allowed us to have:
- same engine in CLI (can generate HTML and open in browser), VSCode extensions, SaaS
- have a way to describe plot visualization / representation as a declarative spec that can be then used in all those products (plot spec). We were exploring plotly and AFAIU there was no easy way to do the same
- it's quite comprehensive and community is responsive, the project is maintained
To name a few downsides from our experience:
- DSL is quite complicated. It requires some time to master it. It hurts the adoption. In our case I don't see that many users doing custom plots / templates - majority is using pre-baked built-in stuff or use Python and export as SVG.
- In our case some features were missing (and are still missing) - exponential average - that is most commonly used to smooth ML training curves.
[1] https://vega.github.io/vega-lite/
[2] https://dvc.org/doc/user-guide/experiment-management/visuali...