How can you manage model versioning in production?

1 Why model versioning matters

Model versioning is not only about keeping track of the code and data that generate your models, but also about capturing the metadata and artifacts that describe your model's performance, configuration, and dependencies. This way, you can ensure the reproducibility, traceability, and auditability of your models, as well as the consistency and quality of your predictions. Model versioning also enables you to experiment with different versions of your models, compare their results, and choose the best one for your use case. Moreover, model versioning allows you to roll back to a previous version of your model if something goes wrong in production, or if you need to comply with changing regulations or requirements.

2 How to version your models

When versioning your models, there are certain elements to consider in order to ensure the model is optimized. These include a unique identifier, such as a timestamp, hash, or semantic version number; a description of the model's purpose and expected outcomes; a record of the code, data, parameters, and hyperparameters used; a collection of the model's artifacts; a summary of the model's performance metrics; a list of the model's dependencies; a specification of the model's inputs and outputs; and documentation of the model's deployment and monitoring details. All of these elements will help you create an effective version of your model.

4 How to track your model versions

Storing your model versions is not enough, you also need to track them throughout their lifecycle, from development to deployment to monitoring. Tracking your model versions can help you understand the lineage and provenance of your models, analyze the impact of changes, communicate and collaborate with stakeholders, and optimize and improve performance. To do this, you can use a logging or reporting tool such as TensorBoard or Streamlit to visualize and compare model metrics and artifacts, a metadata or catalog tool like Kubeflow Metadata or DataHub to store and query model metadata and lineage, and an experiment or project management tool like Comet or Neptune to organize and manage experiments and projects.

5 How to update your model versions

Updating your model versions can be a crucial part of versioning models to ensure they are up-to-date with the data, code, or requirements. This process involves retraining the models with new or updated data, code, parameters, or hyperparameters, and deploying the new model versions to production. Additionally, you may need to switch or roll back to a previous model version if the new version fails or underperforms. To help with this process, tools such as Airflow or Dagster can automate and orchestrate model training, testing, and deployment processes. Feature stores or databases like Feast or Hopsworks can store and manage model features. And model governance or compliance tools like Superwise or Fiddler can monitor and control the model behavior and performance while enforcing policies and rules for model updates.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?