FLAML VS FEDOT

Compare FLAML vs FEDOT and see what are their differences.

Our great sponsors
  • WorkOS - The modern identity platform for B2B SaaS
  • InfluxDB - Power Real-Time Data Analytics at Scale
  • SaaSHub - Software Alternatives and Reviews
FLAML FEDOT
9 4
3,618 595
2.8% 1.5%
8.3 8.5
6 days ago about 21 hours ago
Jupyter Notebook Python
MIT License BSD 3-clause "New" or "Revised" License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

FLAML

Posts with mentions or reviews of FLAML. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-08-14.

FEDOT

Posts with mentions or reviews of FEDOT. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-06-17.
  • The experience of the AutoML application in hackathons
    2 projects | /r/hackathon | 17 Jun 2021
    ITMO University's Natural Systems Simulation Lab is integrating hackathons into education and research. One of its first results is an application of the AutoML framework called FEDOT that allows obtaining the result that impressed the expert board and brought the lab’s team a victory at a river flood forecasting hackathon organized by the Ministry of Emergency Situations. The final model is hybrid and combined several data-driven models and equation-based domain-specific models.

What are some alternatives?

When comparing FLAML and FEDOT you can also consider the following projects:

autogluon - AutoGluon: AutoML for Image, Text, Time Series, and Tabular Data

nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

H2O - 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.

ML-For-Beginners - 12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all

LightAutoML - LAMA - automatic model creation framework

Made-With-ML - Learn how to design, develop, deploy and iterate on production-grade ML applications.

nitroml - NitroML is a modular, portable, and scalable model-quality benchmarking framework for Machine Learning and Automated Machine Learning (AutoML) pipelines.

automl - Google Brain AutoML

question_generation - Neural question generation using transformers

auto-drive - A machine learning AI in Python trained to play my car game.

autokeras - AutoML library for deep learning

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