MLOps and LLMOps: Managing AI lifecycle and governance risks

Machine learning operations (MLOps) and Large Language Model operations (LLMOps) are evolving to address the unique challenges of deploying and maintaining artificial intelligence in production environments.

MLOps pipelines provide automation and engineering rigor to manage the full lifecycle of a model, from data collection and feature engineering to deployment and automated retraining. These pipelines are essential for maintaining model accuracy and reliability as user behavior and datasets shift over time.

As LLMs introduce new complexities—such as prompt versioning, vector databases, and RAG pipelines—the emergence of LLMOps presents a risk of ‘shadow AI.’ This occurs when teams build unreviewed pipelines outside of central platform governance. Experts suggest that LLMOps should not exist as an isolated silo but should instead be integrated into well-run platforms using existing ecosystem tools to ensure oversight and scalability.