MLOpsprinciper

MLOpsprinciper (Swedish for "MLOps principles") denotes the core guidelines that govern how organizations design, deploy, monitor, and govern machine learning systems in production. The concept aligns with MLOps as a discipline and emphasizes repeatable, principled processes across teams.

The principles cover the full lifecycle of machine learning work, from data collection and preparation to model training, deployment, monitoring, and eventual decommissioning. They prioritize reproducibility, reliability, and accountability, recognizing that ML systems differ from traditional software due to data dependence, model drift, and regulatory considerations.

Key principles include reproducible experiments and data lineage; versioning of data, code, configurations, and trained models; automated ML pipelines with continuous integration and continuous deployment for models; robust monitoring and alerting for performance, data quality, and drift; governance and risk management, including privacy, security, and compliance; explainability and bias mitigation; and clear ownership, roles, and collaboration between data scientists, developers, and operators.

In practice, organizations implement these principles through tools and practices such as versioned data stores, feature stores, model registries, experiment tracking, and automated testing. The aim is to make ML systems auditable, scalable, and resilient to changing data and requirements, while enabling safer deployment and ongoing improvement of production models.