Model Versioning

Model versioning is a system for centrally recording and managing the multiple versions of machine learning and deep learning models generated throughout the training, updating, and deployment lifecycle. Similar to source code version control (e.g., Git), it enables tracking of which data, algorithm, hyperparameters, and training environment were used to produce each model, ensuring reproducibility and quality control. Because model development produces a large number of models through minor changes and retraining, failing to manage them properly leads to problems such as: • Uncertainty about which model is running in production • Inability to roll back to a previously better-performing model • Lost training data or parameter history, making reproduction impossible To avoid these risks, model versioning records information including: • The model files themselves (e.g., .pkl, .pt, .onnx) • The version of the dataset used for training • Versions of the code and libraries used • Hyperparameter settings • Evaluation metrics such as accuracy and loss • Experiment notes and metadata Representative tools include MLflow, DVC (Data Version Control), Weights & Biases, and SageMaker Model Registry — often integrated with experiment management and model registries. Model versioning is a core component of MLOps (Machine Learning Operations) and is an essential foundation for reproducibility, reliability, and continuous improvement in team development and production deployment.