Run ML and analytics

without compromising on privacy, security or performance

Simple stats through to GenAI

Train and deploy anything from simple statistics to the most recent generative AI models.

The Compute Gateway allows for compliant preprocessing, computation and aggregation across boundaries

Fitting right into any ML workflow

Connect any data type to any machine learning model served by standard MLops tooling with the Compute Gateway.

Stay in your preferred frameworks and libraries such as PyTorch, scikit-learn, Hugging Face, or native Python.

Make it seamless to gain insights from sensitive data.​

Minimal code port

Use ready-made model implementations available within the Apheris Model Registry:

  • Federation-ready
  • Designed for privacy-preserving, secure and compliant training
  • Implemented in standard ML frameworks such as PyTorch

Automatically federate learning and analytics across Compute Gateways without changing your code.

Seamlessly connect any ML workflow

Specify computational requirements such as compute resources, datasets and ML models with any standard workflow and send it off for training.

Want to productize your data assets?

Learn how you can commercialize your data and build new ML-powered products. Talk to us about governed, private, secure data access for ML.