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 if a new version performs poorly. Reproducibility is achieved by keeping track of the exact configuration and data used for each version, allowing others to replicate the results. Collaboration is enhanced by providing a clear history of changes, making it easier for team members to understand the evolution of the model.

Several tools and frameworks support modellversionering, such as MLflow, DVC, and Git. MLflow, for example, provides a platform for managing the end-to-end machine learning lifecycle, including experimentation, reproducibility, and deployment. DVC (Data Version Control) integrates with Git to version control large files and datasets, ensuring that the data used for training models is tracked and versioned.

In summary, modellversionering is essential for maintaining the quality and reliability of machine learning models. By tracking and managing different versions, practitioners can ensure that their models are reproducible, collaborative, and robust.