FLAML
FEDOT
Our great sponsors
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 |
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
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Elevate Your Python Skills: Machine Learning Packages That Transformed My Journey as ML Engineer
4. FLAML
- Show HN: AutoML Python Package for Tabular Data with Automatic Documentation
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what is the future of ML.NET?
Improved AutoML - Again, with collaboration from Microsoft Research, we used FLAML to update our existing AutoML solutions. What does this mean for you? You're using the latest techniques but all you need is a problem to solve and some data to get started.
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Automated Machine Learning (AutoML) - 9 Different Ways with Microsoft AI
For a complete tutorial, navigate to this Jupyter Notebook: https://github.com/microsoft/FLAML/blob/main/notebook/flaml_automl.ipynb
FEDOT
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The experience of the AutoML application in hackathons
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?
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