SAP-HANA-AutoML
autoai
| SAP-HANA-AutoML | autoai | |
|---|---|---|
| 1 | 3 | |
| 48 | 166 | |
| - | 2.4% | |
| 1.5 | 5.4 | |
| over 1 year ago | 3 months ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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SAP-HANA-AutoML
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Introducing my AutoML library
I'm excited to introduce you my (and another great developer) school diploma project. Fully open-source, Automated Machine Learning Library! We are beating built-in AutoML in SAP famous product. GitHub repository (waiting for your stars): https://github.com/dan0nchik/SAP-HANA-AutoML Web-application for users who don't want to code: https://share.streamlit.io/dan0nchik/sap-hana-automl/main/web.py
autoai
- [P] AutoAI – A framework to find the best performing AI/ML model
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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/...
What are some alternatives?
evalml - EvalML is an AutoML library written in python.
nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
ds2ai-python - The MLOps platform for innovators 🚀
adanet - Fast and flexible AutoML with learning guarantees.
sigopt-server - Open Source version of SigOpt API, performing hyperparameter optimization and visualization
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.
automlbenchmark - OpenML AutoML Benchmarking Framework
optuna-examples - Examples for https://github.com/optuna/optuna
ds2 - Easiest way to use AI models without coding (Web UI & API support)