MLOps in Practice: Principles, Maturity Levels, and an Adoption Plan

Machine learning projects often begin as successful experiments and become difficult to operate when data changes, models are retrained, and production behavior must be monitored. The practical answer is to manage data, code, features, models, infrastructure, approvals, and monitoring as one repeatable lifecycle. MLOps provides that lifecycle. It... Weiterlesen