A model registry is a kind of repository that is used to both store and train machine learning models. In the procedure of ML model monitoring, model registries can simplify the work of tracking the machine learning models when those models move through the lifecycle of machine learning.
This includes the journey from training to the deployments of production and also retirement. In this procedure of ML model monitoring, the model registry helps by storing information regarding the training jobs and the data that are required for the creation of the model.
The task of tracking those factors is vital to developing lineage for those ML models. In this way, a proper model registry helps a function that is analogous to artifact repositions and version control systems for the software that is traditional.
How does a model registry work?
Each model is stored in a model registry and is given a unique identifier. That identification is known as UUID or a model ID. You can use the model ID for ML and Data Science teams to refer to particular models for proper execution and comparing purposes.
Why is a model registry essential in ML model monitoring?
You cannot track every model you create, including all the metadata and versions, even if you have a feature store, automated pipelines, and also a platform for model serving. So, you need a model registry. Discussed below is how a model registry can help in machine learning.
- It helps in tracking the versions of the models
Sometimes you may face a problematic situation where you cannot be sure about which model is the producer of the results that you are viewing. This can take place if your team changes a particular model without updating the number of the version in the model registry.
The names of the files usually do not carry a lot of information that is needed for tracking down a particular model. On the other hand, sources like internal documentation can become inaccurate or out of date.
Here comes the need for a model registry. A model registry can enable you to properly track a particular version of the models that can be updated with every change. So, you can easily find out where each model or version has been used.
- It offers an updated overview
A model registry includes dashboards, through which you can view and filter those models across the entire organization. You can find out what models exist, are there any released versions of those models, or even they are in production or not. This can eventually help you in managing multiple models and projects simultaneously.
- It helps in easier automation
Without the assistance of a model registry, the tool that helps in model serving cannot make automatic requests like “please let me get the up-to-date version of the model that helps in object-detection.” While releasing an update, one will need a manual or partially manual workflow to implement that model into production.
However, with the help of a model registry, you can easily complete the task of automation.
- It helps in auditing
While working with ML models, if something goes wrong, the method of following the trail becomes really difficult without a model registry. The reason is, with the help of a model registry, you can easily track what went wrong, every model which created the predictions, in what kind of environment those models were running in, and many more. Thus, through proper auditing, you can easily fix all the issues with your models. Therefore, the machine registry is a vital part of the procedure that is known as ML model monitoring.