Scalable Kubernetes Infrastructure for AI Platforms

Chapter 1. Introduction

AI, especially generative AI, has followed a similar adoption cycle to many new technologies. Those organizations producing cutting-edge technologies and those in or adjacent to the tech space typically have been quicker to adopt this technology, while other industries have had a slower rate of adoption. However, more recent advancements have helped business leaders realize that they must figure out how to leverage generative AI for their businesses or risk being left behind by their competitors.

Now, enterprises are expending more resources to leverage AI for their businesses. This often takes the form of teams of data scientists implementing proofs of concept (POCs) of AI-based applications for their businesses.

A particularly common proof of concept project in the enterprise is building chatbots. Typically, these projects make use of retrieval-augmented generation (RAG), combining proprietary data with off-the-shelf large language models (LLMs) to give the chatbot expertise on a specific problem domain.

But these enterprises are often facing a difficult challenge: they have POCs of AI-based applications for their businesses, but they can’t move them into production. In fact, the vast majority of these projects never make it to production.

To improve the success rate of these POCs and improve the return on investment of AI initiatives in the enterprise, businesses must develop a better understanding of the challenges that arise when running AI-enabled ...