modellversionering
Modellversionering, also known as model versioning, is a practice in machine learning and data science where different versions of a model are tracked and managed. This process is crucial for maintaining the integrity and reproducibility of machine learning projects. Each version of a model represents a specific state of the model's development, including the data used, the algorithms applied, and the hyperparameters tuned.
The primary reasons for modellversionering include ensuring reproducibility, facilitating collaboration, and enabling rollback to previous versions
Several tools and frameworks support modellversionering, such as MLflow, DVC, and Git. MLflow, for example, provides
In summary, modellversionering is essential for maintaining the quality and reliability of machine learning models. By