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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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.
Individualized machine learning
for every customer
Models learn from each other
without exchange of raw data
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.
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.
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.
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
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.
“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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