MLOps Mognadsnivåer

MLOps combines machine-learning development with software delivery and operations. Maturity models organize capabilities into stages, helping teams discuss current practices and next improvements. Google's MLOps framework, for example, distinguishes manual processes, pipeline automation and more automated CI/CD/continuous-training practices. Other organizations use different labels and dimensions, so a level number should always be tied to the model being used. At an early stage, data preparation, training and deployment may depend on notebooks and manual handoffs. This can work for exploration but makes results difficult to reproduce and release consistently. A next step is to build repeatable pipelines, version inputs and outputs, run tests and evaluation automatically, and maintain a model registry. Later capabilities may automate CI for pipeline code, CD for validated model artifacts, and CT to create candidates when data or schedules warrant. Automation is not an end in itself. A team can have sophisticated pipelines that repeatedly train on poor data or ship a harmful model. Maturity includes monitoring, ownership, governance, reproducibility, rollback, security and clear feedback paths. Determine which capability addresses the current bottleneck: a small team may gain more from reliable evaluation and deployment documentation than from a complex orchestration platform. Assessments should be evidence-based. Ask whether data and code versions are recorded, whether tests and quality gates run consistently, whether releases are reversible, and whether live performance is monitored. Avoid assigning a single score that masks differences between areas. A maturity model can guide investment, but it is not a certification or proof that a system is safe, fair or effective. Reassess as team size, risk, model use and regulatory obligations change. The aim is dependable delivery appropriate to context, not reaching the highest stage for its own sake.