Developing AI applications for large-scale enterprise environments, particularly in critical sectors like finance and healthcare, presents a unique set of challenges. A key to simplifying this process for developers is the implementation of a robust platform architecture. This session will delve into how a standardized platform can streamline the development of reliable and scalable AI applications, enabling developers to concentrate on crafting solutions that address user needs.

Key focus areas include:

•     The flexibility to switch between private and public Large Language Models (LLMs), along with a contingency plan for LLM provider service disruptions.
•     Simplifying the construction of context through the sequential chaining of model inputs and outputs across different LLMs.
•     Employing standardized LLMOps/MLOps techniques for monitoring both predictive and generative AI models.
•     Enhancing predictive AI capabilities with generative AI features and vice versa, fostering a complementary relationship between these two AI paradigms.

By attending this talk, developers will gain insights into building AI applications more efficiently, leveraging the power of a standardized platform to focus on innovation and problem-solving.