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Generative artificial intelligence is still in its infancy, but it already brings irresistible promise to help businesses serve their customers.
That makes optimal hybrid data management critical to any organization with a strategy that entails using third-party software-as-a-service AI solutions with its proprietary data.The public cloud offers scalable environments ideal for experimenting with LLMs. However, full-scale LLM deployment can be prohibitively expensive in the cloud. And while LLMs are only as good as their data, sending sensitive or regulated data to cloud-based LLMs presents significant privacy and compliance risks.
One organization’s experience demonstrates how hybrid cloud-based data management can incorporate public customer data in real time while protecting confidential company and customer information.One of the largest financial institutions in Southeast Asia, Singapore-based, wanted to use AI and machine learning to enhance the digital customer experience and improve its decision making. It used a hybrid cloud platform to do so.
Combining the vast capabilities available on the public cloud with the portability of its private platform helped the bank securely train its AI models and derive more accurate inferences from its outputs.
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