[th0ma5]

I unfortunately would not recommend running this in production. I don't see anything like debugging or centralized or distributed logging either way, no performance metrics either from a raw code or stats performance measurements. I could go on, but there are many software projects out there that try to productionalize ML and know nothing about stats, but this is the opposite, it seems knowledgeable about statistical methods more than most of these kinds of things, but doesn't seem to have a production mindset.

[nidhaloff]

Thanks for the feedback! When I first started the project, it was not thought for production. Just for fast prototyping and experimenting with no efforts at all. However, users liked the tool and started requesting more features including support for serving models and eventually deploying (e.g this issue https://github.com/nidhaloff/igel/issues/62)

I agree with your point of vue. However, igel is fairly new and evolving fast. Using igel to serve trained model is a new feature that was implemented in the new release so igel has a long way to go in order to be a solid product for production use.It will surely get more mature with time.

Finally, notice that I didn't recommend running it in production. Just mentioned that it is possible and takes no efforts at all. However, if the user generated a trained model then anything can be done with it from there. Technically, the user can implement his/her own server and use the model as wanted. Obviously, users should do that if they want more control.