LLMOps

Chapter 1. Introduction to Large Language Models

The rise in popularity of large language models (LLMs) is no accident; they’re transforming how we interact with technology and pushing the boundaries of what machine learning models can do.

But here’s the catch: while these models are impressive, scaling them up and managing them in production is no walk in the park. The leap from a research project to a fully fledged, reliable tool is filled with obstacles. We’re talking about meeting enormous computational requirements, managing complex data, and ensuring that everything runs smoothly and securely whether you are self-hosting or using proprietary models.

Before we dive into the nitty-gritty of LLM operations, it’s important to understand why and how these models came to be. Knowing their origins and trajectory helps us appreciate the challenges we face when predicting their behaviors in production.

The evolution of LLMs reflects a series of incremental innovations, each addressing specific limitations of previous models. Early models were limited in scope and required extensive human input for even basic tasks. With advancements in architecture, such as the shift from recurrent neural networks (RNNs) to transformers, and the scaling of model sizes, LLMs have become more sophisticated. This evolution has brought about new challenges, such as managing massive amounts of data and ensuring efficient training processes.

So, let’s get into it.

Some Key Terms

There are three terms ...