autogluon VS automlbenchmark

Compare autogluon vs automlbenchmark and see what are their differences.

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autogluon automlbenchmark
8 3
7,413 391
2.4% 2.0%
9.6 6.4
10 days ago 19 days ago
Python Python
Apache License 2.0 MIT 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.

autogluon

Posts with mentions or reviews of autogluon. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-04-25.

automlbenchmark

Posts with mentions or reviews of automlbenchmark. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-08-24.
  • Show HN: Web App with GUI for AutoML on Tabular Data
    4 projects | news.ycombinator.com | 24 Aug 2023
    Here is benchmark done by independent team of researchers https://openml.github.io/automlbenchmark/

    I think most of overfitting is avoided with early stoppoing technique.

    The underfitting can be avoidwd with using large training time.

  • Show HN: AutoAI
    5 projects | news.ycombinator.com | 12 Nov 2021
    Your list excludes most of well-known open-source AutoML tools such as auto-sklearn, AutoGluon, LightAutoML, MLJarSupervised, etc. These tools have been very extensively benchmarked by the OpenML AutoML Benchmark (https://github.com/openml/automlbenchmark) and have papers published, so they are pretty well-known to the AutoML community.

    Regarding H2O.ai: Frankly, you don't seem to understand H2O.ai's AutoML offerings.

    I'm the creator of H2O AutoML, which is open source, and there's no "enterprise version" of H2O AutoML. The interface is simple -- all you need to specify is the training data and target. We have included DNNs in our set of models since the first release of the tool in 2017. Read more here: https://docs.h2o.ai/h2o/latest-stable/h2o-docs/automl.html We also offer full explainability for our models: https://docs.h2o.ai/h2o/latest-stable/h2o-docs/explain.html

    H2O.ai develops another AutoML tool called Driverless AI, which is proprietary. You might be conflating the two. Neither of these tools need to be used on the H2O AI Cloud. Both tools pre-date our cloud by many years and can be used on a user's own laptop/server very easily.

    Your Features & Roadmap list in the README indicates that your tool does not yet offer DNNs, so either you should update your post here or update your README if it's incorrect: https://github.com/blobcity/autoai/blob/main/README.md#featu...

    Lastly, I thought I would mention that there's already an AutoML tool called "AutoAI" by IBM. Generally, it's not a good idea to have name collisions in a small space like the AutoML community. https://www.ibm.com/support/producthub/icpdata/docs/content/...

  • Show HN: Mljar Automated Machine Learning for Tabular Data (Explanation,AutoDoc)
    3 projects | news.ycombinator.com | 5 Jan 2021
    I'm also curious how does it compare! The package will be included in the newest comparison done by OpenML people https://github.com/openml/automlbenchmark

    I have some old comparison of closed-source old system

What are some alternatives?

When comparing autogluon and automlbenchmark you can also consider the following projects:

FLAML - A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.

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

autokeras - AutoML library for deep learning

auto-sklearn - Automated Machine Learning with scikit-learn

adanet - Fast and flexible AutoML with learning guarantees.

MindsDB - The platform for building AI from enterprise data

imbalanced-regression - [ICML 2021, Long Talk] Delving into Deep Imbalanced Regression

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

tabnet - PyTorch implementation of TabNet paper : https://arxiv.org/pdf/1908.07442.pdf

Ray - Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

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InfluxDB - Power Real-Time Data Analytics at Scale
Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
www.influxdata.com
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