MLOps — definition

MLOps (Machine Learning Operations) is the set of practices that operationalize ML and AI models in production — covering CI/CD pipelines for model updates, automated testing, performance monitoring, data versioning, and rollback procedures. MLOps applies to both traditional ML models and LLM-based systems.

LLMOps is the LLM-specific extension of MLOps, addressing prompt versioning, output evaluation pipelines, safety testing, and the management of retrieval infrastructure. Organizations shipping AI without MLOps practices accumulate silent model debt that surfaces as mysterious accuracy degradation.

Related terms

Model Drift — Model drift is the degradation of an AI model's accuracy over time as the real-world data distribution it is applied to shifts away from the distribution it was trained on.

Fine-Tuning — Fine-tuning is the process of training an existing AI model on additional task-specific data so its weights adapt to a narrower domain.

Inference — Inference is the process of running a trained AI model to produce outputs from inputs.