BMMA framework is capable of scaling to multiple modalities such as tabular, sentiment data, time series, and computer vision data. The architecture of BMMA is centred around the main component, Brain (Facade Design), which manages all internal parts.
The package Brain-AutoML is part of thesis created by Chandravesh chaudhari, Doctoral candidate at CHRIST (Deemed to be University), Bangalore, India under the supervision of Dr. Geetanjali purswani.
- Highly customisable
- provide separate Json for customisation
- Saves time
- Automate monotonous tasks
- Provides replicable results
This project is available at PyPI. For help in installation check instructions
python3 -m pip install brain−multiple−modalities−automl - AutoGluon
- AutoKeras
- AutoSklearn
- TPOT (Tree-based Pipeline Optimization Tool)
- H2O.ai
- ML Jar
- PyCaret Sentiment Analysis models
- BERT
- RoBERTa
all kinds of contributions are appreciated.
- Improving readability of documentation
- Feature Request
- Reporting bugs
- Contribute code
- Asking questions in discussions
- Web based GUI