To advance business objectives and create potential value from generative AI (genAI), businesses may need to leverage a variety of public and proprietary large language models (LLMs), add-ons, application programming interfaces (APIs), data stores, and business applications. There’s far more to it than just plugging in data and letting a model run.
But all these moving parts add extra vulnerabilities atop each part’s unique vulnerabilities. A unified approach can help resolve these issues and many others by integrating the work across the AI ecosystem. Here are three key advantages of working under a unified and data-driven AI ecosystem.
- Increased security: AI models of all types, includinggenAI, pose unique security challenges including but not limited to prompt injection and data poisoning. Many companies use multiple forms of AI either independently or combined into a system—but either course multiplies the security issues your team must combat. A unified and data-driven AI ecosystem that instantly folds AI models into a single secure platform is a more efficient and comprehensive way to deal with this expanded threat surface.
- Endless scalability: By using a flexible tech stack in a unified platform, companies can quickly launch a range of LLMs from OpenAI, AWS, GCP, and open source—as well as self-hosted models—and have them all fully integrated with AWS and Amazon services. This allows instant and endless scalability for each model as needed.
- Better customizability: A unified, model-agnostic platform is highly flexible and agile by nature. Even so, some customization is almost always required to fully meet a company’s unique needs. Open-source components are often the best choice for building and customizing AI web applications like chatbots. Using such a unified and data-driven AI ecosystem to pull models and customizations together is a smart and logical approach.
GenAI models are still new, and many companies are experimenting with them on a variety of use cases before launching their own AI or customizing a commercial AI model. Using a unified platform enables more efficient exploration, development, and deployment of a variety of AI models.
EPAM AI DIAL Platform is a prime example of an enterprise-grade AI ecosystem. It offers a unified user interface designed to leverage a full array of public and private LLMs, add-ons, APIs, datastores, and business applications that coexist seamlessly with existing workflows.
Applications and add-ons can be implemented within a secure and scalable framework via several diverse approaches, such as LangChain, LLamaindex, Semantic Kernal, or even custom code. The platform also aggregates multi-cloud libraries and an extensive toolkit.