MLOps to LLMOps: What Changes and What Stays
Explore the transition from traditional MLOps to LLMOps by understanding what foundational practices remain and what new components and workflows emerge. Learn how semantic meaning, stochastic outputs, and unstructured data redefine monitoring, evaluation, and risk management in production LLM systems.
Imagine we are operating a mature MLOps pipeline for a credit scoring system.
The system predicts whether a loan applicant is likely to default based on structured features such as income, credit history, and debt-to-income ratio. The pipeline is in a stable state, with versioned features, models deployed through CI/CD, and monitoring tools that detect drift.
We use tools like
If metrics like average applicant income or prediction accuracy drop significantly, we can drill down to investigate. Now we apply the same operational logic to an LLM-powered chatbot. The system receives thousands of free-form text queries each day.
One user asks: What is the PTO policy? Another asks: Can I take next Friday off?
Statistically, these sentences look totally different (different length, different words). But semantically, they are identical. Conversely, The bank is running low on cash (financial), and The river bank is running low on water (environmental) share the statistically similar phrase bank is running low on, but they refer to completely different semantic domains (finance vs. geography) and have opposite contextual meanings.
Traditional MLOps tools are often unable to accurately detect semantic drift. This can become a problem because we rely on metrics that measure how data looks (statistics) rather than what it means (semantics) when monitoring advanced models like LLMs.
It would be incorrect to assume that LLMOps is just MLOps with bigger models. It is MLOps adapted to a system where semantics, stochasticity, and unstructured data define the runtime behavior. In this lesson, we map the traditional MLOps stack to LLM-powered systems.
We identify which practices remain, which components must be replaced, and which entirely new workflows and risks emerge.
The foundation: What stays
Before analyzing the new components, we must first examine the foundational principles. LLMOps is still a subset of software engineering. The bottom layers of the stack ...