MLopsin

MLopsin is a framework that defines a structured approach to operating machine learning systems throughout their lifecycle. It builds on the principles of MLOps by emphasizing end-to-end governance, reproducibility, and scalable deployment of models in production.

Key concepts include data versioning and lineage, feature stores, model registries, experiment tracking, and automated testing. MLopsin advocates continuous integration and continuous deployment for models, infrastructure as code, and policy-driven control planes to enforce security, compliance, and explainability.

Architectural patterns under MLopsin favor modular components, metadata-driven pipelines, and event-driven workflows. Components such as data validation services, model quality gates, monitoring dashboards, and drift detection are integrated to support quick rollbacks, canary releases, and rapid remediation of production issues.

In practice, MLopsin is described in theoretical and vendor-agnostic discussions as a blueprint for cross-functional collaboration among data scientists, engineers, and governance teams. Proponents argue it helps reduce operational risk and accelerate value, while critics warn that adding new standards can increase complexity.

See also MLopsin, data versioning, model registry, feature store, and continuous delivery.