SCALE AI ACROSS CUSTOMERS WITH DECENTRALIZED MACHINE LEARNING

    Create and manage machine learning models on distributed systems, at scale and without sacrificing data privacy or security.

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    SCALE AI ACROSS CUSTOMERS WITH DECENTRALIZED MACHINE LEARNING

    Decentralized Machine Learning Solution

    mlx enables on-the-edge machine learning over cross-company systems through a paradigm shift in data science:
    Instead of data, models are exchanged and combined. 

    Systems Element

    Individualized machine learning
    for every customer

    Decentral Element

    Models learn from each other
    without exchange of raw data

    Integration Element

    Easy integration
    into existing infrastructure

    mlx features

    Leverage powerful ML models built on Federated Learning for best-in-class results. mlx automatically evolves ML models across decentrally stored data (on-premise, private cloud, ..).

    Compare the evaluation results of your deployed ML models and centrally monitor the performance metrics in real time.

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    Status, validity and exchange of the local ML models can be managed across all instances.

    Easily set up ML models in the local environment via Docker and remotely deploy the leaf node in minutes. Afterwards, observe the leaf's hardware and performance in the central mlx dashboard.

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    Status, validity and exchange of the local ML models can be managed across all instances.

    Easily set up ML models in the local environment via Docker and remotely deploy the leaf node in minutes. Afterwards, observe the leaf's hardware and performance in the central mlx dashboard.

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    Organize and manage your customized ML jobs: Integrate tailored preprocessing steps, create or utilize existing ML models and put the pipeline into action within a few clicks.

    Keep track of changes in your local data and retrain models in the local environments automatically when the changes are significant. Handle and manage data schemes directly from the dashboard.

    Seamlessly integrate FedML into your existing ecosystems and make use of Keras libraries by embedding them in mlx with a few code snippets only

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    Flexible integration with existing systems Flexible integration with existing systems

    Flexible integration with existing systems

    Dockerized modules with REST APIs allow for easy integration in existing ecosystems.

    mlx strives to integrate all of the common frameworks to work locally and in a decentralized manner.

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    “Organizations that want to share data, but are concerned about privacy, should explore a federated learning approach. This allows data to be shared yet not revealed across organizations. […] There is a small yet growing list of vendors using various approaches in that space, including […] prenode.” (Gartner, 2019)

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