The API is based on the deep-translator python package (https://github.com/nidhaloff/deep-translator).

It is one tool that supports multiple translators and therefore, very easy and convenient to use.

If you want to check the code, here is the link to the repo: https://github.com/nidhaloff/deep-translator-api

Hi all, this is a work in progress that I wanted to share. The goal of this app is to store online in your app, which wine did you find good during a wine tasting session. I'm still working on new features. I just wanted to hear a feedback or criticism from you. Thanks in advance

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?

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.

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 ;)

Hi, thanks but how does this link to igel or to AI/ML

Hi all,

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?

Thanks for the reply. Of course I'm not "expecting" or forcing companies to pay! It's more of I'm hoping or I hope for support in the future.

Also of course I know everything about open source licenses, so as I said I'm not forcing nothing. Thanks for your hints I will try to apply them and see if it would help ;)

More recommendations that I hope are useful:

* If you write a blog post, try to add some graphical representation of the problem and the result. If it's not in the program, make it clear that you used other software to graph it (gnuplot? Excel?) But an image is worth a thousand words.

* Take a look at minimaxir posts. https://news.ycombinator.com/from?site=minimaxir.com It's not the same kind of problems, but some post can give you ideas.

* Can you program solve the problems in Kaggle? Is Kaggle happy if you use their examples/problem and post a solution? It can be another source of ideas, but be careful.

PS: It's nice that you are adding an GUI version. I like to use a GUI version and use the CLI only for weird case or to automate the workflow once it's stabilized.

Last year I built deep-translator https://github.com/nidhaloff/deep-translator

I wanted a tool where multiple translators are integrated and where I can get translations from different sources but only using one tool. I then tried to build a cross platform mobile app using python (which is not the best language for this, I know) https://github.com/nidhaloff/Translator-pp

Probably the best project I built/started last year is the machine learning package igel: https://github.com/nidhaloff/igel

This is awesome. Definitely, the best showHN I ve seen this year! Keep it up ;)

As a developer, I want to ask which programming languages did you used to build this & why. I think you didn't open sourced the tool or am I wrong?

Thanks! Clojure all the way. Why? Because lisp is awesome :) I haven't made it open source yet, and I'm not entirely sure I will, depends what's best for the project, if making it a paid game on Steam is the way to go... But even if I do I would have to refactor the code somewhat first.

Awesome. I'm a fan of functional programming too :D

I definitely think that there is a possibility to turn this project to a business. I would find it ideal if you can do both, turn it into a business and at the same time open source the code. Maybe you ll find a way. Good luck ;)