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