Home Services MLOps and DataOps Automation Model Versioning
MLOps and DataOps Automation

Model Versioning

Versioning software is a well-established practice, and the same principles apply to machine learning models. Managing and tracking different versions of models throughout their lifecycle is essential. This practice ensures reproducibility, facilitates collaboration, and allows for effective rollback and comparison between model iterations.
Make An Enquiry

How model versioning is different from software versioning?

Model versioning tracks code, data, hyperparameters, model architecture, and training environments, ensuring reproducibility and comprehensive performance metrics. Unlike regular software versioning, it accommodates frequent iterations and varied datasets. This approach brings benefits such as enhanced collaboration, precise performance comparison, and the ability to quickly revert to previous versions, ensuring reliable and high-quality models.

Systematic tracking and management

Implementing version control systems ensures systematic tracking and management of model versions, allowing for organized documentation and retrieval of specific versions.

Reproducibility and consistency

By maintaining detailed records of data, hyperparameters, and code, model versioning ensures that each version can be reproduced accurately, maintaining consistency across experiments and deployments.

Enhanced collaboration

Model versioning supports collaboration by providing a clear history of model iterations, facilitating communication and coordination among team members working on different versions.

Rollback and comparison

The ability to easily roll back to previous model versions and compare performance metrics enables quick resolution of issues and informed decision-making, ensuring stable and reliable model performance.

In highly regulated industries like pharma, proper model versioning is essential. While software versioning is straightforward, incorporating data adds significant complexity. It requires tracking the entire model's development path, including experimentation history and data used throughout the process. However, once implemented, it provides excellent value by ensuring compliance, improving auditability and model management. It also opens door for further analysis of your model development performance.

Overview

Version control involves tracking and managing changes made to machine learning systems. Model versioning is a type of version control focused on tracking changes made to ML models in a machine learning system. There are three types of versioning in a model version system: data version control, which tracks changes to data, code versioning, which tracks modifications to source code, and model versioning, which tracks modifications to the ML model. By using a version control system (VCS), teams can keep better track of changes made to the source code data or model version. This allows them to reproduce results, debug issues, and collaborate more effectively. Ultimately, model versioning tracks multiple versions of an ML model so it’s easier to roll back to previous versions when required.

Experts in the field

Konrad Madej
Principal Software Architect
Go to Expert Spotlight

Helping clients
drive digital change globally

Discover how our comprehensive services can transform your data into actionable business insights,
streamline operations, and drive sustainable growth. Stay ahead!

Explore our Services

See Technologies We Use

At the core of our approach is the use of market-leading technologies to build IT solutions that are cloud-ready, scalable, and efficient. See all
Snowpark ML
MLFlow
DVC

Latest Related Insights

Show more related

Let's talk about a solution

Our engineers, top specialists, and consultants will help you discover solutions tailored to your business. From simple support to complex digital transformation operations – we help you do more.