AIMultiple ResearchAIMultiple Research

Machine Learning Model Versioning: Benefits & Tools in 2024

Cem Dilmegani
Updated on Feb 13
2 min read

90% of top businesses have an ongoing investment in AI and 79% of executives say AI makes business processes easier. However, developing and deploying AI and ML models in enterprise has its challenges. The process requires constant effort to achieve efficient and reliable functioning of an ML system, or ML projects are prone to failures.

Companies have started to leverage MLOps practices to automate and streamline ML-related processes. Model versioning is an important component of these practices, and its main purpose is to make AI and ML reproducible and increase model performance.

What is model versioning?

Model versioning is the practice of tracking and managing the changes in ML models over time.

ML model development is an experimental process involving lots of trials and errors with:

  • Model types,
  • Model parameters and hyperparameters,
  • Datasets,
  • Code, etc.

to achieve the best performing version of a model. Model versioning is the process of tracking these versions and enabling them to be accessed at any time.

How is it different from data versioning?

Although data versioning and model versioning can be used interchangeably, their scope is different:

  • Data versioning keeps track of and stores the changes in training dataset over time.
  • However, the changes that are tracked with model versioning are not limited to the changes in the data set. It also tracks the changes in hyperparameters, algorithms, metrics, and artifacts in addition to data sets. So, model versioning covers a more broad set of components.

What are the benefits of model versioning?

As a crucial aspect of the MLOps practices, model versioning improves ML processes from various aspects:

Makes AI reproducible

Reproducibility refers to obtaining the same results when the same dataset and algorithm are performed in the same environment. It is important for both AI research and AI applications in the enterprise because it enables the application of a model in different contexts and provides predictability. Model versioning helps to store all versions of different components of a model and thus improves reproducibility.

Improves collaboration

Version control tools such as Git help software developers collaborate on the same project and keep track of changes made by multiple developers. Model versioning serves the same purpose in ML model development, helps to follow-up on all the changes in an ML model, and makes the ML processes transparent for all team members. Model versioning is an essential feature as your ML models scale and you have multiple teams managing multiple models.

Increases reliability

Changes in incoming data and other components can cause ML models to produce unexpected results or break down completely. Storing different versions of models allows data scientists to compare different versions and revert to a stable version when an error occurs.

How to implement model versioning?

Implementing model versioning requires creating distinct repositories, branches, and integration branches for each model. There are open source tools for model versioning, such as:

  • Data Version Control: The DVC can keep track of different versions of the data and model and store them both on-premises and in the cloud.
  • ML Metadata: MLMD is a Python library that enables recording and retrieving ML model metadata.
  • ModelDB: ModelDB is an open-source tool to version ML models and to track ML metadata across the model lifecycle.

There are also data science or MLOps platforms that offer various tools for a comprehensive machine learning operationalization. 

Feel free to check our article on MLOps tools and our sortable/filterable list of MLOps platforms for more on MLOps tools. If you have other questions, we can help:

Find the Right Vendors
Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
Follow on

Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

To stay up-to-date on B2B tech & accelerate your enterprise:

Follow on

Next to Read


Your email address will not be published. All fields are required.