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OpenML
beta_2
Machine learning, better, together
Python API 0.10 is released
HACKATHON
Bring your own data, bring your own algorithms, or build cool new features.
OpenML on Open Science Radio
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As machine learning is enhancing our ability to understand nature and build a better future,
it is crucial that we make it transparent and easily accessible to everyone in research, education and industry.
The Open Machine Learning project is an inclusive movement to build an open, organized, online ecosystem for machine learning.
We build open source tools to discover (and share)
open data from any domain,
easily draw them into your favourite machine learning environments,
quickly build models alongside (and together with) thousands of other data scientists, analyse your results against the state
of the art, and even get automatic advice on how to build better models. Stand on the shoulders of giants and make the world a better place.
Watch the 1-minute introduction.
Sign me up!Watch the 1-minute introduction.
Signing up is free and brings you lots of powerful features.
All public data is always openly available.
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Identifying the most appropriate machine learning techniques and using them optimally can be challenging for the best of us.
OpenML is a place where you can share interesting datasets with the people who love to analyse data, and build the best
solutions together, saving you valuable time, increasing your visibility, and speeding up discovery. OpenML links
data to algorithms and people, so you can build on the state of the art and learn to teach machines to learn better.
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Tell people which learning problem you want to solve (e.g., classify observations) by
creating tasks describing your goals. This allows meaningful collaboration,
easy benchmarking or different methods, and direct comparison to the state of the art.
OpenML tasks are machine-readable, allowing tools to automatically get the data and train and evaluate models, so that you can focus on the science.
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OpenML is directly integrated into the most popular machine learning tools, but you can also build your own integrations
with the Python, R, Java, and C++ APIs, or program against the REST API.
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The OpenML integrations make sure that all uploaded results are linked to the exact (versions) of datasets, workflows, software, and the people involved.
We generate predictions locally using exact procedures, and evaluate them server-side so that results are directly comparable and reusable in further work.
Wherever possible, we extract clear descriptions of machine learning workflows and models.
