Is MLOps a methodology or a tool?

Amanda

MLOps is primarily a methodology that combines practices, processes, and culture to manage the lifecycle of machine learning models from development to production. It is supported by various tools for automation, versioning, deployment, and monitoring, but it is not a single tool itself. MLOps focuses on reliability, scalability, and continuous improvement of ML systems. In your experience, do you see MLOps more as a structured process or as a collection of tools working together?

Michael

In my experience, MLOps is best understood as a structured process that is enabled by a collection of tools, not defined by them. The tools (for tracking experiments, versioning data/models, CI/CD, feature stores, deployment, and monitoring) are important, but without a clear lifecycle process—ownership, approvals, reproducibility standards, release criteria, and feedback loops—you end up with disconnected automation that is hard to govern. When treated as a process, MLOps standardizes how models move from research to production, how data and model changes are audited, how drift and performance are monitored, and when retraining happens. The tools then plug into each stage to reduce manual effort and improve consistency. So practically, we use “tools working together,” but the value comes from the shared process and operating model.