nidhaloff

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.

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 ;)

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

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 ;)

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?

A simple but yet powerful tool that allows users to train, evaluate and use machine learning models with few clicks or by running simple commands.

Igel UI is the official GUI cross platform desktop app that allows you to interact with igel: https://github.com/nidhaloff/igel with a few clicks instead of using the terminal.

I started this project based on people's feedback on the igel tool. The project is still under heavy development. I just made a beta release yesterday. Stay tuned

I want to thank all people for their feedback and feature ideas.

Igel made it so far since the last release. Cross validation, hyperparameter search, interactive mode, automatic code generation and more features have been implemented. We also started working on a cross platform desktop app for igel. Stay tuned

We are already working on a gui ;) stay tuned

Hi, can you please explain more, or you can open an issue and explain the expected functionality there

Hi, can you explain how you imagine the functionality with SQLite and why SQLite specifically

Thanks. Stay tuned, a lot is coming soon

Thank you. I appreciate it

Indeed interesting :D

OMG this is funny :D I wasn't aware about this, thnx. Looks like it's a thing and the company can even skyrocket in the near future! I only read the about us section there.

I also noticed they pronounce the name differently. Actually, igel is a german word and is pronounced "Eagle" but they pronounce it as I-jeel

Hi, thanks for your feedback. hmm any machine learning project has to start with a dataset. Most of the time you will have to construct it (or take an existing one and update it) manually. Sure there are tool that generate a dummy dataset for you but that would be just for playing a world and certainly not for a real world use/production.

We are actually working on adding support for text, excel and json format in igel.

We already implemented some of the famous preprocessing methods in igel, which you can use by providing them in the yaml file.

Now about preprocessing libraries, I personally use numpy, pandas and some of sklearn functionality to preprocess data. Furthermore, I use matplotlib and seaborn for some visualisation & further analysis.

Someone posted this tool earlier in the comments too. I was surprised since I never heard of it and find it great!

However, I think it is only for building deep learning models and does not have any general ML support or am I missing something? If yes then that fact makes it very different from igel as a tool

Wow! this is great! I didn't know that such a tool exists, thanks for posting it here. The python & AI community are moving really fast, it's crazy!

However, It looks like the ludwig tool is about deep learning and not ML, or am I wrong? It looks like there is no support for ML models or am I missing something

I didn't try it yet, I just read the get started section but looks really great for training deep neural networks.

Thanks for your feedback. Stay tuned, we are working on an integrated gui tool written in python too.

Hi, we should be careful with the feature you are talking about. The results from all machine learning algorithm can be very misleading and probably some models will overfit the data.

So, if you throw some data and fit all machine learning models on it and then compare the performance. You will probably receive misleading values since different models require different tuning approaches. It's not as easy as you said it, you can't just feed data (also depends on the data) to models and expect to get the best model at the output.

One approach I can think of here is to integrate cross validation and hyperparameter tuning with your suggestion. However, I can imagine that this can be computationally expensive. I will take it into consideration as an enhancement for the tool. Thanks for your feedback

It's a german word and means Hedgehog.

It's funny we were discussing a name for the project and we wanted to make an abbreviation from some words that make sense, so we started throwing ideas spontaneously. At the end we wanted to make an abbr for these words: "Init, Generate, Evaluate Machine Learning".

IGEL made sense for us then since it's a german word too. Easy to say, type and remember ;)

Well, "both technical and non technical users" right?

Hi thanks a lot. I received positive interactions on github from the community, however, your comment is the first encouraging feedback I ve got here :D so, I appreciate it.

I will take your suggestion into consideration. You are right, there should be more real-world examples that will help users get started and see how this can be useful.

The thing is, I started the project two weeks ago, so it still relatively new. I ve been coding day n night because the idea got me excited. I published the first stable release this week. However, there are new features that will be implemented in the next releases.

Hi, I want to clear up some points. First, it is not intended for non technical folks, this was never claimed! However, even if it was, we are currently working on a gui, where (non technical)users can run it by writing a simple cmd in the terminal.

Second, I'm a technical user, in fact this is my daily work and we build this tool for reasons that were mentioned in the docs/readme, so you can check it out.

Third, you mentioned understanding YAML Format. Really? I mean yaml is the most understandable format any person can understand. I can never imagine that a person cannot learn yaml in 30 min at most.

Finally, yes sklearn is great and well documented but did you checked how many libraries are out there that represent basically a wrapper to make it easier/abstracter to write sklearn code? you ll be surprised.

As discussed in the official repo & docs, it is a much cleaner approach to gather your preprocessing & model definition parameters/configs in one human readable file/place, where you can manipulate it easily. Re-run experiments, generate drafts, building proof of concepts as fast as possible, than to write code. At the end of the day, we all have different opinions, you can still write code of course. The tools are there to help.