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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
Conversational AI
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 SDK
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
Article
Refine large language models to boost AI performance
Open AI's ChatGPT is very popular, yet not ready for enterprise adoption due to accuracy, security & privacy concerns. By refining large language models this can be tackled. For organizations that operate in a regulated space, federated learning can be used to train on distributed data to unlock value while maintaining privacy.
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Beyond MLOps - How Secure Data Collaboration Unlocks the Next Frontier of AI Innovation
DevOps and MLOps are common methodologies in every company that wants to become software and data science driven by weaving AI into the core fabric of their business. Read what is required to securely collaborate with partners on data and AI at scale.
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Buyer's Guide to Secure Data Collaboration
Secure data collaborations with multiple parties are the next source of getting a competitive edge. However, only a few data leaders know what to look for to get projects going. This buyer's guide will help you evaluate platforms and highlight considerations for AI and analytics across organizational boundaries.