Awesome
New way for building ML pipelines!
We are working on new way for Python visual programming. We developed desktop application called MLJAR Studio. It is a notebook based development environment with interactive code recipes and managed Python environment. All running locally on your machine. We are waiting for your feedback.
It has code recipes to build ML pipelines with MLJAR AutoML.
<p align="center"> <img alt="mljar AutoML" src="https://raw.githubusercontent.com/pplonski/pplonski/main/media/piece-of-code.png" width="77%" /> </p>AutoML Web App 🤖
<p align="left"> <a href="https://github.com/mljar/mljar-supervised">🚀 AutoML</a> <span> • </span> <a href="https://github.com/mljar/mercury">📓 Mercury</a> <span> • </span> <a href="https://github.com/mljar/automl-app/issues">🤝 Issues</a> <span> • </span> <a href="https://twitter.com/MLJAROfficial">🐦 Twitter</a> <span> • </span> <a href="https://www.linkedin.com/in/aleksandra-p%C5%82o%C5%84ska-42047432/">👩💼 LinkedIn</a> <span> • </span> <a href="https://mljar.com">🌐 MLJAR Website</a> </p>This is a Web Application designed to train Machine Learning pipelines using MLJAR AutoML, specifically tailored for tabular data. All the generated models are compressed into an archive format, allowing their reuse to compute predictions in batch mode.
This repo consists of three notebooks:
- notebook for training AutoML with simple UI,
- advanced notebook for training AutoML with more advanced UI (you can select feature engineering methods, algorithms, validation strategy, and evaluation metric),
- notebook for computing predictions.
The Web App harnesses the capabilities of mljar-supervised to construct the Machine Learning pipeline with AutoML. This involves the automation of several key tasks:
- data preprocessing,
- features engineering,
- algorithm selection & tuning,
- ML models explanations,
- automatic documentation.
The Web App is created directly from Jupyter Notebooks with Mercury framework.
Demo
https://github.com/mljar/automl-app/assets/6959032/3363631a-2187-44cd-94a8-3cbd5418de98
Online demo
The Web App is available online at automl.runmercury.com. Input data upload is limited to 1MB.
<kbd> <img src="https://github.com/mljar/automl-app/blob/main/media/web-app-online.png" alt="AutoML Web App online"></img> </kbd>Run locally 🖥️
Please run the below commands to run Web App locally. It requires Python >= 3.8.
pip install -r requirements.txt
mercury run
Training Notebook 📓
If you would like to increase the input file limit, please change the cell:
data_file = mr.File(label="Upload CSV with training data", max_file_size="1MB")
and set your max_file_size.
Please change the following cell to increase training time:
time_limit = mr.Select(label="Time limit (seconds)", value="60", choices=["60", "120", "240", "300"])
Times are in seconds. Please just increase the values.
<kbd> <img src="https://github.com/mljar/automl-app/blob/main/media/notebook.gif" alt="AutoML training notebook"></img> </kbd>Training models in Web App
Please upload a CSV file with training data, select input features & target, and click Start training.
All models created during the training are available for download as a zip file:
<kbd> <img src="https://github.com/mljar/automl-app/blob/main/media/web-app-download.gif" alt="AutoML models available for download"></img> </kbd>Advanced Training Notebook 💪
Please use advanced mode if you would like to tweak AutoML parameters:
<kbd> <img src="https://github.com/mljar/automl-app/blob/main/media/web-app-advanced.gif" alt="Advanced AutoML training notebook"></img> </kbd>👩💼🐦 Connect with Us on LinkedIn & Twitter
Stay up-to-date with the latest updates about MLJAR 🎨🤖 by following us on Twitter (MLJAR Twitter) and LinkedIn (Aleksandra LinkedIn & Piotr LinkedIn). We look forward to connecting with you and hearing your thoughts, ideas, and experiences.