WE BUILD A MANAGED FEDERATED MACHINE LEARNING SOLUTION SO YOU CAN SCALE AI ACROSS YOUR CUSTOMERS

Create and manage machine learning models on distributed systems safely, ready-to-integrate and at scale.

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WE BUILD A MANAGED FEDERATED MACHINE LEARNING SOLUTION SO YOU CAN SCALE AI ACROSS YOUR CUSTOMERS

Managed Federated Machine Learning Solution

Integrate mlX with your existing environment to scale machine learning across multiple systems

Utilization of federated data for best-in-class ML models

No centralization of data required - privacy by design

Management and deployment of ML models in decentral systems

Federated Learning

How mlx Works

Continuous improvement and deployment of ML models across multiple entities.

01
Local Model Training

Machine learning models are automatically trained in the local environment on the local data.

02
Model Centralization

For each distributed environment, secured and encrypted ML models are sent to their central node.

03
Model Fusion

The central node fuses the individual machine learning models into one superior model.

04
Model Redistribution & Deployment

The fused model is redistributed and further enhanced on the local data. Customized ML models are then deployed in the local environment. Now, the cycle starts again.

01
Local Model Training

Machine learning models are automatically trained in the local environment on the local data.

mlX features

mlx automatically evolves ML models across decentral stored data (on-premise, private cloud, hybrid-cloud).

ML models can be remotely deployed and are updated automatically.

ML model status, validity, performance, and exchange can be managed across all instances.

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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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