Software

FEDOT is an open-source framework for automated modeling and machine learning (AutoML) problems. This framework is distributed under the 3-Clause BSD license. It provides automatic generative design of machine learning pipelines for various real-world problems. The core of FEDOT is based on an evolutionary approach and supports classification (binary and multiclass), regression, clustering, and time series prediction problems.

Link: https://fedot.readthedocs.io/en/latest/

FLAML is a lightweight Python library for efficient automation of machine learning and AI operations. It automates workflow based on large language models, machine learning models, and optimizes their performance.

Link: https://microsoft.github.io/FLAML/

GAMA is an AutoML package for end-users and AutoML researchers. It generates optimized machine learning pipelines given specific input data and resource constraints. A machine learning pipeline contains data preprocessing (e.g. PCA, normalization) as well as a machine learning algorithm (e.g. Logistic Regression, Random Forests), with fine-tuned hyperparameter settings (e.g. number of trees in a Random Forest). To find these pipelines, multiple search procedures have been implemented. GAMA can also combine multiple tuned machine learning pipelines together into an ensemble, which on average should help model performance. At the moment, GAMA is restricted to classification and regression problems on tabular data. In addition to its general use AutoML functionality, GAMA aims to serve AutoML researchers as well. During the optimization process, GAMA keeps an extensive log of progress made. Using this log, insight can be obtained on the behavior of the search procedure.

Link: https://openml-labs.github.io/gama/master/

H2O is an in-memory platform for distributed, scalable machine learning. H2O uses familiar interfaces like R, Python, Scala, Java, JSON and the Flow notebook/web interface, and works seamlessly with big data technologies like Hadoop and Spark. H2O provides implementations of many popular algorithms such as Generalized Linear Models (GLM), Gradient Boosting Machines (including XGBoost), Random Forests, Deep Neural Networks, Stacked Ensembles, Naive Bayes, Generalized Additive Models (GAM), Cox Proportional Hazards, K-Means, PCA, Word2Vec, as well as a fully automatic machine learning algorithm (H2O AutoML).

Link: https://docs.h2o.ai/h2o/latest-stable/h2o-docs/automl.html

Hyperopt-Sklearn (Hyperparameter optimization for Sklearn) is a Python library for hyperparameter-optimization-based model selection among machine learning algorithms in the Scikit-learn package. The main goal of Hyperopt-Sklearn is to automate and ease the process of hyperparameter tuning for machine learning models. It utilizes Bayesian optimization techniques to decrease the complexity of hyperparameter tuning and speed up the optimization process. It is a valuable tool for tuning hyperparameters and improving performance of Scikit-learn models without manual intervention.

Link: https://hyperopt.github.io/hyperopt-sklearn/

LightAutoML is an open-source Python library aimed at automated machine learning. It is designed to be lightweight and efficient for various tasks with tabular, text data. LightAutoML provides easy-to-use pipeline creation that enables: automatic hyperparameter tuning, data processing; automatic typing, feature selection; automatic time utilization; automatic report creation; and easy-to-use modular scheme to create your own pipelines.

Link: https://lightautoml.readthedocs.io/en/latest/

Ludwig is a low-code framework for building custom AI models like LLMs and other deep neural networks. The Ludwig allows you to build custom models with ease. A declarative YAML configuration file is all you need to train a state-of-the-art LLM on your data and its support for multi-task and multi-modality learning. You can also optimize for scale and efficiency, since it also provides automatic batch size selection, distributed training (DDP, DeepSpeed), parameter efficient fine-tuning (PEFT), 4-bit quantization (QLoRA), and larger-than-memory datasets. By supporting hyperparameter optimization, explainability, and rich metric visualizations, you retain full control of your models down to the activation functions. It is modular and extensible and is engineered for production (Docker, HuggingFace).

Link: https://ludwig.ai/latest/

ML-Plan is a Java-based free software library for AutoML and provides a tool to optimize machine learning pipelines in WEKA or Sklearn. It is one of the functionalities of AILibs, a modular collection of Java libraries related to automated decision making.

Link: https://starlibs.github.io/AILibs/projects/mlplan/

MLBox is a powerful AutoML Python library that provides fast reading and distributed data preprocessing/cleaning/formatting, highly robust feature selection and leak detection, accurate hyperparameter optimization in high-dimensional space, state-of-the-art predictive models for classification and regression (Deep Learning, Stacking, LightGBM, etc.), and prediction with model interpretation.

Link: https://mlbox.readthedocs.io/en/latest/