The mission of the AI Model Share Platform is to provide a trusted non profit repository for machine learning model prediction APIs (python library + integrated website at modelshare.org). A beta version of the platform is currently being used by Columbia University students, faculty, and staff to test and improve platform functionality.
In a matter of seconds, data scientists can launch a model into this infrastructure and end-users the world over will be able to engage their machine learning models.
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Launch machine learning models into scalable production ready prediction REST APIs using a single Python function.
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Details about each model, how to use the model's API, and the model's author(s) are deployed simultaneously into a searchable website at modelshare.org.
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Deployed models receive an individual Model Playground listing information about all deployed models. Each of these pages includes a fully functional prediction dashboard that allows end-users to input text, tabular, or image data and receive live predictions.
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Moreover, users can build on model playgrounds by 1) creating ML model competitions, 2) uploading Jupyter notebooks to share code, 3) sharing model architectures and 4) sharing data... with all shared artifacts automatically creating a data science user portfolio.
pip install aimodelshare
Make sure you have conda version >=4.9
You can check your conda version with:
conda --version
To update conda use:
conda update conda
Installing aimodelshare from the conda-forge channel can be achieved by adding conda-forge to your channels with:
conda config --add channels conda-forge
conda config --set channel_priority strict
Once the conda-forge channel has been enabled, aimodelshare can be installed with conda:
conda install aimodelshare
or with mamba:
mamba install aimodelshare