Version Control Your ML Model Deployment With Git using Modelbit
Introduction
Version control is critical to all development processes, allowing developers to track software changes (code, configurations, data, etc.) over time.
Moreover, it facilitates collaboration between team members, enabling them to work together on the same codebase without interfering with each other's work.
In the context of data teams, version control can be especially crucial when deploying models.
It enables them to identify precisely what changed, when it changed, and who changed it – crucial information when trying to diagnose and fix issues that arise during the deployment process or if models start underperforming post-deployment.
In such cases, git-based functionality can offer quick rollback to previous versions.
Therefore, in this article, I will show how you can power your model deployment with Git functionality.
More specifically, we'll use the git-functionality of Modelbit for deployment and sync GitHub with Modelbit for collaborative functionalities.
Let's begin
