ML Model Registry: What It Is and Why It Matters?
A model registry is a list of models that have been trained and made available for use by other developers. A machine learning (ML) model registry is a centralized repository where you can find, try, and download ML models. It allows you to search for models based on specific criteria, such as the type of data being used or the type of task being performed.
A model registry is useful for implementing trustless AI systems, where users do not have to trust the provider of AI services. For example, imagine that you have an ML model that predicts whether customers are likely to purchase your product or not. You can publish this model on the registry and allow other users to download it for free. The user could then verify that the published model is indeed from your company by using its public key from the registry. This allows them to verify that they are not being tricked into using a different company’s model.
The most common use cases for a machine learning model registry include:
Providing access to a large number of models that can be used for prediction tasks in production applications. The training data used to build these models may not always be available to your organization, but you can still use them without having to train your own models from scratch. This is particularly useful if your company doesn't have enough data scientists to train new models every time they need one.
Providing access to pre-trained ML models that have already been trained on large datasets with thousands or millions of samples. These models are often much more accurate than those built using smaller datasets because they've had more time and computational power dedicated to training them.
Importance of Model Registry
The purpose of a model registry is to ensure that the models are correctly identified and indexed. A model registry can be used for various purposes, such as:
1) Identifying and Indexing - Ensure that a model is correctly identified and indexed by multiple systems. This can be useful for example when you want to use multiple databases with different schemas.
2) Track the provenance of your data - This is useful when you want to understand where your data came from and whether there are any issues with it.
3) Curate your data - For example, you could create a custom relationship between two models in order to create a new model based on them.
4) Less errors - It reduces the risk of errors in data management systems caused by using obsolete versions of models. Many software companies do not actively support older versions of their software, so if you continue to use an old version, it might stop working when you upgrade your operating system or other software packages on your computer.
You also may need to upgrade your database server or client computers if they do not support older versions of databases or web browsers needed by your application.
Key Features of Model Registry
1) Central Repository
The model registry is the central repository of all models in your organization. It enables users to manage and share models across their organization. You can use the model registry to manage and share your own models, as well as those shared by others.
2) Model Versioning
You can version your models using the model registry. This enables you to track changes made to a model over time, including changes made by other users.
3) The registry notifies the user for every event in the ML lifecycle. When a model is uploaded or updated in Model Registry, it sends out notifications about these events to users who are subscribed for them. These notifications help users keep track of their models at all times and also help them know when something goes wrong with them.
