Implementation/Tutorial of using Automated Machine Learning (AutoML) methods for static/batch and online/continual learning

MIT License

AutoML-Implementation-for-Static-and-Dynamic-Data-Analytics

Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation

mljar-supervised

The world's cleanest AutoML library ✨ - Do hyperparameter tuning with the right pipeline abstractions to write clean deep learning production pipelines. Let your pipeline steps have hyperparameter spaces. Design steps in your pipeline like components. Compatible with Scikit-Learn, TensorFlow, and most other libraries, frameworks and MLOps environments.

Apache License 2.0

Neuraxle

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

AutoML-Implementation-for-Static-and-Dynamic-Data-Analytics

mljar-supervised

Neuraxle

h2o-3

AutoML-Implementation-for-Static-and-Dynamic-Data-Analytics

mljar-supervised

h2o-3