automlbenchmark
mercury
automlbenchmark | mercury | |
---|---|---|
3 | 77 | |
380 | 3,770 | |
2.4% | 0.7% | |
6.7 | 8.5 | |
8 days ago | 14 days ago | |
Python | Python | |
MIT License | GNU Affero General Public License v3.0 |
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.
automlbenchmark
-
Show HN: Web App with GUI for AutoML on Tabular Data
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
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)
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
mercury
-
Ask HN: What's the best charting library for customer-facing dashboards?
I'm build dashboards in Jupyter Lab. My plotting libraries are Altair, matplotlib, seaborn, Plotly - all work well in notebook.
My favorite is Altair. It provides interactivity for charts, so you can move/zoom your plots and have tooltips. It is much lighter than Plotly after saving the notebook to ipynb file. Altair charts looks much better than in matplotlib. One drawback, that exporting to PDF doesn't work. To serve notebook as dashboard with code hidden, I use Mercury framework, you can check example https://runmercury.com/tutorials/vega-altair-dashboard/
disclaimer: I'm author of Mercury framework https://github.com/mljar/mercury
-
mercury VS solara - a user suggested alternative
2 projects | 13 Oct 2023
-
Show HN: Web App with GUI for AutoML on Tabular Data
Web 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.
-
streamlit VS mercury - a user suggested alternative
2 projects | 8 Jul 2023
- GitHub - mljar/mercury: Convert Jupyter Notebooks to Web Apps
-
[P] Opinionated Web Framework for Converting Jupyter Notebooks to Web Apps
The GitHub repository https://github.com/mljar/mercury
-
Show HN: Opinionated Web Framework for Converting Jupyter Notebooks to Web Apps
We are working on open-source web framework Mercury that converts Python notebooks to Web Apps.
It is very opinionated:
- it has no callbacks - we automatically re-execute cells below updated widget
- it has no layout widgets, all input widgets are always in the left sidebar
Thanks to above decisions you don't need to change notebook's code to have web app and fit to the framework.
The simplicity of the framework is very important to us. We also care about deployment simplicity. That's why we created a shared hosting service called Mercury Cloud. You can deploy notebook by uploading a file.
The GitHub repository https://github.com/mljar/mercury
Documentation https://RunMercury.com/docs/
Mercury Cloud https://cloud.runmercury.com
- Show HN: Build Web Apps in Jupyter Notebook with Python Only
-
[OC] Analyzing 15,963 Job Listings to Uncover the Top Skills for Data Analysts (update)
Analysis was done in Jupyter Notebook with Python 3.10, Pandas, Matplotlib, wordcloud and Mercury framework.
-
[OC] Data Analyst Skills in need based on 15,963 job listings
Analysis was done in Jupyter Notebook with Python 3.10 kernel, Pandas, Matplotlib, wordcloud and Mercury framework to share notebook as a web application with widgets and code hidden. Gif created in Canva.
What are some alternatives?
autogluon - Fast and Accurate ML in 3 Lines of Code
streamlit - Streamlit — A faster way to build and share data apps.
nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
voila - Voilà turns Jupyter notebooks into standalone web applications
autokeras - AutoML library for deep learning
papermill - 📚 Parameterize, execute, and analyze notebooks
MindsDB - The platform for customizing AI from enterprise data
voila-gridstack - Dashboard template for Voilà based on GridStackJS
adanet - Fast and flexible AutoML with learning guarantees.
jupytext - Jupyter Notebooks as Markdown Documents, Julia, Python or R scripts
mljar-supervised - Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
awesome-streamlit - The purpose of this project is to share knowledge on how awesome Streamlit is and can be