Best ML Model Registry Tools

A model registry is a central repository that is used to version control Machine Learning (ML) models. It simply tracks the models while they move between training, production, monitoring, and deployment. It stores all the predominant information such as:

As the model registry is shared by multiple team members working on the same machine learning project, model governance is a major advantage that these teams have. This governance data tells them:

Usually, while working in a team, different team members tend to try out different things, and only a few of them are finalized and pushed to the version control tool they use. The model registry helps them solve this issue as each team member can try their own versions of models, and they will have a record of all the things they have experimented with throughout the project journey.

This article will discuss the model registry tools and evaluation criteria for such tools. You will also see a comparison of different model registry tools, such as:

So let’s get started!

Evaluation criteria for choosing model registry tools 

The model registry is an important part of MLOps platforms/tools. There are plenty of tools available in the market that can fulfill your ML workflow needs. Here is an illustration that classifies these tools on the basis of their specialization.

The products on the bottom right are focused on deployment and monitoring; those on the bottom-left focus on training and tracking. Those at the very top aim to cover every aspect of the ML lifecycle, while those in the middle-top do most or all of the spectrum with leaning one way or another.

To visualize it even more precisely, let’s have a look at another image:

From the above image, it can be inferred that tools like Kubeflow and other cloud providers are the most balanced and cover every stage of an ML pipeline development equally. Specialized tools like Neptune and Polyaxon are closest to their axis, i.e., majorly focused on model training. 

NOTE: Aforementioned evaluation criteria for these tools is subjective to the features these tools had at that point in time (November 2021). Many of these tools have moved much beyond their area of specialization in the past year, so take this discussion with a pinch of salt.

However, there are some evergreen factors that are integral to determining a registry tool’s effectiveness. From my own experience, some of them are:

Installation and integration

Choosing the right model registry tool is often influenced by how it would be installed and what kind of integrations it would offer. Usually, organizations choose the tools based on their development environment. For example:

Integrations can be a major worry for firms that are dedicated to their current choices in terms of their technological stack. If an organization is using some continuous integration tool, they might prefer the model registry tool that easily blends in. 

Ease of automation

Another requirement of a model registry tool is how easily the development team can make use of that tool.

Auto-ML tools have less flexibility for customizations while Low-Code tools provide both custom and automation options finally, Code-First tools only provide a writing code facility. You can choose a tool based on your requirement.

Updated model overview and model stages tracking 

The entire purpose of a model registry tool is to provide an easy overview of all the versions of models that the development team has tried. While selecting the tool, you must remember that the tool must provide the model overview of each version at every stage. Tracking models extend beyond development; it is done for maintenance and enhancement in staging and production as well. The machine learning model lifetime including:

must be tracked by the model registration tool.

Competence in managing the model dependencies

The model registry tool must have compatibility with all the dependencies the ML model needs. You should check the dependencies competence for the Machine Learning libraries, Python version, and data. If you are working on some use case that requires a special ML library and the registry tool does not support it, that tool would not make much sense for you.

Providing the flexibility of team collaboration

You may evaluate whether you and your team can collaborate on the registered model or not. If the model registry enables you to work with your team on the same ML model, then you can choose that tool.

Thus, you can follow the evaluation criteria to select the best model registry tool according to your requirements.

Model registry tools

Here are a number of model registry tools that are used across the industry:

1. Neptune.ai

Neptune provides a central processing unit to store, log, compare, display, query, and organize all metadata. It can be utilized by the development and research team to store the metadata and conduct various experiments. One can also use Neptune to track these experiments and the model registry for controlling the model development lifecycle. It enables debugging and metrics comparison easier by generating tables automatically during the run process.

 Some of the features of Neptune’s model registry include:

2. MLflow

An open-source platform that you can use for managing the ML model lifecycle. MLFlow enables you to track the MLOps life cycle with the help of its APIs. It provides model versioning, model lineage, annotations, and transitions from development to deployment functionalities.

Some features of MLflow model registry are as follows:

3. Amazon SageMaker

Developers use Amazon SageMaker for complete control of the ML development lifecycle. You can catalogue production models, associate metadata, and manage versions and approval status of models with the SageMaker registry. 

First, you create a version of a model and specify its respective group. Besides, you could use an inference pipeline to register the model with variables and container specifications. Then you may create new model versions using AWS Python SDK. Moreover, you can also deploy the model out of the model registry using AWS. You can deploy the trained Machine Learning model with real-time interference and low latency to SageMaker endpoints. This deployed model can be monitored using the Amazon SageMaker Model Monitor feature. 

Some features of the Amazon Sagemaker model registry are as follows:

4. Comet

Developers can use the Comet platform to manage machine learning experiments. This system allows you to version, register, and deploy the model using its Python SDK Experiment.  

Comet keeps track of model versions and the experiment history of the model. You can check the detailed information of all model versions. Besides, you can maintain ML workflow more efficiently using model reproduction and optimization.  

The feature-rich Comet has various functionalities for running and tracking ML model experiments, including:

5. Verta.ai

You can use the Verta AI tool for the management and operations of the model in one unified space. It provides an interactive UI where you can register the ML models and publish the metadata, artefacts, and documents. Then, to manage the end-to-end experiment, you may connect the model to the experiment tracker. Version control solutions for ML projects are also offered by Verta AI.

Additionally, it enables you to keep track of changes made to data, code, environments, and model configuration. With the audit log’s accessibility, you may also examine the model’s dependability and compatibility at any time. You can also create a unique approval sequence that is appropriate for your project and incorporate it with the selected ticketing system.

Some of the main features of Verta AI’s model registry are:

Comparison of model registry tools

Every model registry tool has different features and performs various unique operations. Hence it is necessary to compare these tools to select the best according to your requirement. The below table will discuss the Model registry tools comparison:

Summary

After reading this article, I hope you now know what model registry tools are and the different criteria that one must look for while selecting a model registry tool. To offer a practical perspective, we also discussed some of the popular model registry tools and compared them with each other in several aspects. Now, let’s wrap the article with a few key takeaways:

With these points in mind, I hope your model registry tool search will be much easier.

References