The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning. Learn more →
Top 23 Automl Open-Source Projects
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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.
22. Ray | Github | tutorial
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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.
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nni
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
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Project mention: pip install remyxai - easiest way to create custom vision models | /r/computervision | 2023-04-25
This seems not very convincing. There are other popular frameworks that provide AutoML with existing datasets (eg https://github.com/autogluon/autogluon)
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Project mention: Featuretools – A Python Library for Automated Feature Engineering | news.ycombinator.com | 2023-09-20
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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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.
I would use H20 if I were you. You can try out LLMs with a nice GUI. Unless you have some familiarity with the tools needed to run these projects, it can be frustrating. https://h2o.ai/
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I really like the simplicity of this framework, and they hit on a lot of common problems found in other agent-based frameworks. Most intrigued by the RAG improvements.
Seems like Microsoft was frustrated with the pace of movement in this space and the shitty results of agents (which admittedly kept my interest turned away from agents for the last few months). I'm interested again because it makes practical sense, and from looking at the example notebooks, seems fairly easy to integrate into existing applications.
Maybe this is the 'low code' approach that might actually work, and bridge together engineering and non-engineering resources.
This example was what caught my eye: https://github.com/microsoft/FLAML/blob/main/notebook/autoge...
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I can't find the TimeGPT-1 model.
LICENSE Apache-2
https://github.com/Nixtla/statsforecast/blob/main/LICENSE
Mentions ARIMA, ETS, CES, and Theta modeling
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igel
a delightful machine learning tool that allows you to train, test, and use models without writing code
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mljar-supervised
Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
Project mention: Show HN: Web App with GUI for AutoML on Tabular Data | news.ycombinator.com | 2023-08-24Web App is using two open-source packages that I've created:
- MLJAR AutoML - Python package for AutoML on tabular data https://github.com/mljar/mljar-supervised
- Mercury - framework for converting Jupyter Notebooks into Web App https://github.com/mljar/mercury
You can run Web App locally. What is more, you can adjust notebook's code for your needs. For example, you can set different validation strategies or evalutaion metrics or longer training times. The notebooks in the repo are good starting point for you to develop more advanced apps.
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lazypredict
Lazy Predict help build a lot of basic models without much code and helps understand which models works better without any parameter tuning
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Awesome-System-for-Machine-Learning
A curated list of research in machine learning systems (MLSys). Paper notes are also provided.
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Project mention: [Project] AMLTK: A framework for building your own AutoML (AutoSklearn authors) | /r/MachineLearning | 2023-12-09
We took some of the lessons learned while building AutoSklearn and AutoPytorch, the good, the bad and the ugly and made a library that to enable the next generation of open-source AutoML tools, to allow them to be research-able but also efficient and scalable. We have some future plans and on-going work with this and we'd like to gather any feedback the community might have!
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metarank
A low code Machine Learning personalized ranking service for articles, listings, search results, recommendations that boosts user engagement. A friendly Learn-to-Rank engine
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sparseml
Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models
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Project mention: Potential of the Julia programming language for high energy physics computing | news.ycombinator.com | 2023-12-04
> Yes, julia can be called from other languages rather easily
This seems false to me. StaticCompiler.jl [1] puts in their limitations that "GC-tracked allocations and global variables do not work with compile_executable or compile_shlib. This has some interesting consequences, including that all functions within the function you want to compile must either be inlined or return only native types (otherwise Julia would have to allocate a place to put the results, which will fail)." PackageCompiler.jl [2] has the same limitations if I'm not mistaken. So then you have to fall back to distributing the Julia "binary" with a full Julia runtime, which is pretty heavy. There are some packages which do this. For example, PySR [3] does this.
There is some word going around though that there is an even better static compiler in the making, but as long as that one is not publicly available I'd say that Julia cannot easily be called from other languages.
[1]: https://github.com/tshort/StaticCompiler.jl
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NOTE:
The open source projects on this list are ordered by number of github stars.
The number of mentions indicates repo mentiontions in the last 12 Months or
since we started tracking (Dec 2020).
The latest post mention was on 2024-04-01.
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A note from our sponsor - WorkOS
workos.com | 17 Apr 2024
Index
What are some of the best open-source Automl projects? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | Ray | 30,879 |
| 2 | best-of-ml-python | 15,284 |
| 3 | nni | 13,708 |
| 4 | autokeras | 9,061 |
| 5 | auto-sklearn | 7,388 |
| 6 | autogluon | 7,050 |
| 7 | featuretools | 7,010 |
| 8 | H2O | 6,705 |
| 9 | automl | 6,143 |
| 10 | FLAML | 3,663 |
| 11 | zenml | 3,638 |
| 12 | statsforecast | 3,519 |
| 13 | adanet | 3,470 |
| 14 | Merlion | 3,248 |
| 15 | igel | 3,080 |
| 16 | mljar-supervised | 2,927 |
| 17 | keras-tuner | 2,821 |
| 18 | lazypredict | 2,661 |
| 19 | Awesome-System-for-Machine-Learning | 2,406 |
| 20 | Auto-PyTorch | 2,269 |
| 21 | metarank | 1,981 |
| 22 | sparseml | 1,967 |
| 23 | PySR | 1,850 |
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