Definition
Model versioning is the process of tracking different versions of ML models during development and after deployment. The process spans giving unique identifiers to model versions and maintaining metadata like run logs, performance metrics, hyperparameters, code changes etc. for each version. With model versioning, developers can save, identify, and return to various versions of a model. This is helpful for tracking changes, testing different versions in various settings, and making improvements or updates without undoing earlier work [1].
For example, Anthropic has released several versions of their large language model Claude since 2023 which include among others Claude 1.3, Claude 2.1, Claude 3.5 Sonnet and the latest at the time of writing this post Claude Sonnet 4.5.
Origin
The concept of
versioning originated from software development, where it was first utilized to
document changes in code and manage software releases. Gradually, as AI and
machine learning developed, the need for versioning expanded to include AI
models and associated data, leading the establishment of the concept of
versioning in LLMOPs.
As AI technologies advanced and the complexity of AI models increased, the need for robust versioning mechanisms became evident. This led to the evolution of versioning in LLMOPs, encompassing not only model versioning but also the management of large-scale datasets, facilitating the reproducibility and traceability of AI experiments [3].
Context and Usage
In Machine
Learning (ML) systems, teams use model versioning to track changes in data,
code, and the model being developed to achieve optimal results.
Why it Matters
The process of model versioning involves rigorously monitoring and managing various iterations of an LLM model over time. This is necessary as it ensures the ability to reproduce specific models and their performance at a later date, which is important for testing and deployment. Secondly, it enables a thorough analysis of modifications made to the models, which provides clearer insights into their evolution and impact [4].
In Practice
Tetrate Agent Router Service (TARS) is a good example of a real-life case study of model versioning in practice. With TARS, organizations can implement advanced versioning strategies including automated canary deployments, performance-based version selection, and intelligent version routing that optimizes for both performance and reliability across their entire AI model portfolio [5].
See Also
Related Model
Training and Evaluation concepts:
- Model Training: Process of teaching an AI model to make predictions by learning from data
- Objective Function: Mathematical function that a model optimizes during training to achieve desired outcomes
- Overfitting: Problem where a model learns training data too well and fails to generalize to new data
- Tuning: Process of adjusting model parameters to optimize performance
- Turing Test: Evaluating machine intelligence
References
- Khandelwal, L. (2024). Versioning Machine Learning Models.
- Deepchecks. (2023). Model Versioning for ML Models: A Comprehensive Guide. In Geeksforgeeks.
- Lark Editorial Team. (2023). Versioning in Llmops.
- Hendricks, R. (2025). What is versioning in LLMOps?
- Tetrate. (2025). Model Versioning.