Fine-tune language models on federated data
Users expect seamless and flawless interactions. Integrating conversational AI into your products requires ML teams to fine-tune their language models to customer interactions. Leverage federated learning to access such sensitive customer data that spans across customers, systems or geographies.
EXAMPLE USE CASES
Telemedicine and patient apps
Train and evaluate language models on sensitive medical information and personal health data while protecting data privacy. Ensure models are explainable, predictable and trustworthy.
FinTech
Collaborate with financial institutes and fine-tune ML models on sensitive customer data. Ensure that the data always stays protected and never leaves the financial institutes' environments.
Personalized advertisement
Gain an unbeatable competitive edge through the ability to leverage sensitive consumer data in a privacy-safe way. Build better and more accurate ad targeting models without moving or aggregating consumer data.
Why conversational AI teams choose Apheris
IP protection of data
Apheris federated infrastructure ensures that data always stays where it resides and under the full control of the data custodian.
Integrated
ML engineers can easily launch their pre-trained language models from, for example, Huggingface and fine-tune them to sensitive data of third-parties.
Scalability
The Apheris Platform scales to very large compute workloads as required for most conversational AI applications.
Compliance
Apheris offers state-of-the art security, privacy and governance to ensure compliance across regulatory requirements.
AdTech
"We need to launch our product on a federated infrastructure – where PII data stays where it is captured and doesn't have to move across borders – there is no other way forward for us."
CTO Top-30 AdTech company
Governed, private, secure
Want to learn more about how Apheris can help with computational access to sensitive data?
What is a Platform for Secure Data Collaboration