How We Reached LLMOps
Understand the historical and theoretical evolution from AI and ML to large language models (LLMs). Learn why traditional MLOps tools fall short for LLM deployment and how unique constraints like cost, latency, and prompt management drive the need for specialized LLMOps practices that focus on external artifacts and operational challenges.
When LLM systems move from prototypes into production, trust, latency, and cost become hard constraints rather than edge cases.
Understanding why these constraints emerged and why they cannot be solved with traditional ML tooling is essential before we design any production architecture. Large language models did not appear in isolation. They are the result of several shifts in how we build intelligent systems.
We transitioned from rule-based logic to statistical learning, from prediction to generation, and from locally owned models to externally hosted APIs.
Each of these shifts changed what we deploy, what we control, and what can fail at runtime. In this lesson, we will trace that evolution step by step. By the end, it should be clear why traditional MLOps assumptions no longer hold, and why operating LLM-powered systems requires a different mindset and toolset.
The evolution from AI to LLMs
We often hear terms like AI, ML, and GenAI used interchangeably, but for an engineer, the distinctions are architectural.
We can visualize these ...