292 karma · joined August 24, 2020
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
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.
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.
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 ;)
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
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 ;)
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?
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
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
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
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.
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
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.
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 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 ;)
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.
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.