Generative artificial intelligence (AI) is still in its early stages, but it already holds great promise for enabling businesses to serve their customers.
Organizations use generative AI to quickly and economically sift through large amounts of their own data to produce highly relevant, high-quality text, audio, images, and other content in response to prompts based on large amounts of training data. can. Hosted open-source Large Language Models (LLMs) also allow organizations to add enterprise data context to their output to produce more reliable responses while reducing false information (“hallucinations”). helps.
The dilemma, however, is that organizations need to provide third-party AI tools with access to company-specific knowledge and proprietary data in order to get more accurate output from generative AI models. And companies that don’t take proper precautions can expose sensitive data to the world.
Optimal hybrid data management is therefore important for organizations with a strategy of using third-party Software-as-a-Service (SaaS) AI solutions that contain their own data.
Harness the power of hybrid cloud
The public cloud provides the perfect scalable environment for LLM experimentation. However, a full-scale LLM deployment in the cloud can be prohibitively expensive. And while the quality of an LLM is determined by data, sending sensitive or regulated data to his cloud-based LLM poses significant privacy and compliance risks.
A private cloud offers the best environment for hosting LLMs, including your own enterprise data, and a cost-effective solution for longer-term LLM deployments than what public clouds offer. Housing LLM in a private cloud enhances data security and protects sensitive information from external threats and compliance issues.
Organizations adopting hybrid workflows can take full advantage of generative AI without sacrificing privacy and security, getting the best of both worlds. Keep your most sensitive data safe on an on-premises platform while benefiting from the flexibility of the public cloud for early experiments.
One organization’s experience demonstrates how hybrid cloud-based data management can incorporate public customer data in real time while protecting sensitive corporate and customer information.
A more personalized experience
Based in Singapore, one of Southeast Asia’s largest financial institutions wanted to use AI and machine learning (ML) to enhance digital customer experiences and improve decision-making. We used a hybrid cloud platform for that.
OCBC has built a single entry point for all LLM use cases. It’s a hybrid framework that seamlessly integrates multiple data sources, including input from thousands of customers and private cloud data lakes that keep customer data safe, to gain real-time insights. Customized according to your own standards.
The bank built a Prompt microservice to access LLMs stored on on-premises servers as well as LLMs available in the public cloud. This is a cost-effective model that allows you to both use public cloud LLMs and host open source LLMs, depending on your environment. Required features and customizations. OCBC saved 80% on the cost of using a SaaS solution by deploying and hosting its own Code Assistant for 2,000 users.
Combining the vast capabilities available in the public cloud with the portability of a private platform, banks were able to safely train AI models and derive more accurate inferences from their outputs.
The platform is integrated with banks’ ML operational pipelines and fits into the larger ML engineering ecosystem. This cloud-based, ML-powered platform will enable OCBC to build its own applications and use the tools and frameworks of choice for data scientists.
The effort has resulted in a more personalized customer experience, higher campaign conversion rates, faster transactions, less data center downtime and an additional S$100 million (US$75 million) annual revenue.
Secure Innovation with Generative AI
Organizations are rushing to adopt generative AI to streamline operations and drive innovation. Enterprises need AI tools that have enterprise-specific context and leverage knowledge from their own data sources.
However, while the technology is still maturing, privacy, security and compliance need not have to be sacrificed. With a hosted open source LLM, businesses have access to the latest features and fine-tune models using their own data, while maintaining control to avoid privacy concerns and limit spending. can.
A hybrid platform allows organizations to leverage the benefits of the public cloud while keeping their proprietary AI-based insights out of the public eye. Hybrid workflows that incorporate vendor-agnostic, open and flexible solutions enable businesses to store and use data wherever and whenever they need it, while at the same time offering significant cost advantages, enabling AI to truly democratized.
Learn more about how you can Use open source LLM with your own data in a secure environment.
