Cool project! Can probably integrate with SimpleML (author) to add persistence and versioning. Happy to help if you're interested
https://github.com/eyadgaran/SimpleML
I worked on a team for 2 years that built a gui tool on top of sklearn.
It's a heavily involved space and feel free to DM me at some point if you want any tips and also I can point you to a few libraries that are being developed by my former team and are open source.
Hi, thanks sure it would be great to exchange information about other libraries. Mainly I'm looking for third party libs that I can integrate with igel for benchmarking or automate deployment. How can I contact you?
- When running "fit", I get an error when I use the "--yaml_file" parameter mentioned in the readme: "Unrecognized argument -> yaml_file". Using "-yml" works
- When using the model for prediction, what format should "path_to_your_test_dataset.csv" be in? Is that a csv in the same format as the training set except for the column we want to predict? Edit: I just found https://github.com/nidhaloff/igel/blob/v0.4.0/examples/data/...
Hi, thanks for your feedback! you are right, the argument/parameter is actually yaml_path and not yaml_file. This is a typo in the docs/readme, thanks for this finding ;)
Igel is a delightful tool to help you create, validate and use machine learning model (also in production) without writing code. You can use the integrated command line or the graphical interface.
Igel uses FastAPI and uvicorn to serve your trained model, due to their high performance.
Hi, this looks interesting to me, but am I correct that it doesn't support image data?
I spotted a typo in the README: "63 different machine learning model in igel" should say "models".
The feature "Supports all state of the art machine learning models" seems absurd. How could it possibly be true? Surely there are many SOTA models not in SKLearn?
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.
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.
