Model Versioning
Model versioning is a systematic approach to tracking, managing, and controlling changes to machine learning models throughout their lifecycle. It involves assigning unique identifiers (versions) to model artifacts, such as trained weights, hyperparameters, and metadata, enabling reproducibility, collaboration, and rollback capabilities. This practice is essential in ML operations (MLOps) to ensure that models can be reliably deployed, monitored, and updated in production environments.
Developers should learn and use model versioning when building and deploying machine learning systems to maintain reproducibility, facilitate team collaboration, and manage model evolution over time. It is critical in scenarios like A/B testing, regulatory compliance, and debugging production issues, as it allows tracking which model version produced specific predictions and enables easy rollback to previous stable versions if errors occur.