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  • Our Free Plans just got better! | Auth0 Icon
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    Build Securely on Azure with Proven Frameworks

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  • 1
    lightning AI

    lightning AI

    The most intuitive, flexible, way for researchers to build models

    ...Find templates (Lightning Apps), modify them and publish your own. Lightning Apps can even be full standalone ML products! Run on your laptop for free! Download the code and type 'lightning run app'. Feel free to ssh into any machine and run from there as well. In research, we often have multiple separate scripts to train models, finetune them, collect results and more.
    Downloads: 4 This Week
    Last Update:
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  • 2
    ADAMS

    ADAMS

    ADAMS is a workflow engine for building complex knowledge workflows.

    ADAMS is a flexible workflow engine aimed at quickly building and maintaining data-driven, reactive workflows, easily integrated into business processes. Instead of placing operators on a canvas and manually connecting them, a tree structure and flow control operators determine how data is processed (sequentially/parallel). This allows rapid development and easy maintenance of large workflows, with hundreds or thousands of operators. Operators include machine learning (WEKA, MOA, MEKA)...
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    Downloads: 4 This Week
    Last Update:
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  • 3
    Guild AI

    Guild AI

    Experiment tracking, ML developer tools

    ...The toolkit is platform-agnostic, running on all major operating systems and integrating seamlessly with existing software engineering tools. Guild AI supports various remote storage types, including Amazon S3, Google Cloud Storage, Azure Blob Storage, and SSH servers.
    Downloads: 0 This Week
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  • 4
    ML workspace

    ML workspace

    All-in-one web-based IDE specialized for machine learning

    ...This workspace is the ultimate tool for developers preloaded with a variety of popular data science libraries (e.g., Tensorflow, PyTorch, Keras, Sklearn) and dev tools (e.g., Jupyter, VS Code, Tensorboard) perfectly configured, optimized, and integrated. Usable as remote kernel (Jupyter) or remote machine (VS Code) via SSH. Easy to deploy on Mac, Linux, and Windows via Docker. Jupyter, JupyterLab, and Visual Studio Code web-based IDEs.By default, the workspace container has no resource constraints and can use as much of a given resource as the host’s kernel scheduler allows.
    Downloads: 2 This Week
    Last Update:
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