In recent years, the use of ML model registries in businesses has grown. Companies often have many machine learning models at various stages. It is important to control, track, and manage these ML models during testing, experimentation, and production stages. Monitoring and managing ML models help improve the efficiency of these ML models. The ML model registry is a system that allows you to store, share, and monitor ML models.
Four benefits of ML model registry in business are discussed in this article. But first, let us discuss in detail what an ML model registry is.
What Is an ML Model Registry?
The machine learning model registry is a repository for storing trained ML models. Machine learning model registry stores data used to build the model training data. The ML model registry performs functions like traditional software version control systems. An example of such a versioning system is Git. The ML model registry allows model developers to publish production-ready models to a central hub for easy access whenever needed. The central hub makes it easy to manage the lifecycle of all the ML models in it.
The key features of an ML model registry are listed below:
- Central storage for all types of models.
- A joint unit for model lifecycle management.
- A graphical user interface (GUI) may be used to look into and test the ML model.
- A programmatic API may be used to retrieve the previous version of the ML model.
Four Benefits of ML Model Registry in Business
- It Accelerates the Business Operations
Business operations are the activities that a company performs daily to add value to the organization and generate sales. The development of ML models like recommender engines can help businesses achieve their ultimate goals such as increasing profit.
It is difficult to identify the model for your production environment. ML model registry bridges the gap between experimentation and production activities. This speeds up the rollout of production models which in turn accelerates the business operations.
Also, the ML model registry saves trained models for quick and easy retrieval. The ML model registry allows software engineers to select the optimal trained model. This allows the model to be quickly tested, validated, and released into production. This makes the production of ML models smooth.
- It Ensures ML Model Security and Governance
ML model security involves securing ML models from vulnerabilities in production. The ML model is used in applications to make important decisions for businesses. Some important applications of ML models in business are sales forecasts, customer segmentation, etc. These ML models need to be secured. The model and its packages used to build the ML model should be scanned for vulnerabilities. It is most important when many packages are used to develop and deploy the model.
The ML model registry can manage specific versions of packages. It can also scan and remove security vulnerabilities in ML models. ML registries help enforce the security concept of least privileged access. This ensures that only authorized users have access to certain model information.
ML model governance is the overall process of how a business controls access, implements policies, and tracks model activity. Model registries help businesses, or technology stakeholders implement formal model governance.
You can create an ML model inventory that lists all ML models. The inventory also contains related data, their usage, interdependencies, and assigned risk levels. The ML model inventory can be easily monitored to ensure governance of all model assets in the organization and infrastructure.
One of the great things about model registries is that they centralize ML model storage. This helps to organize the relevant details so ML model essentials which are development, verification, regulations, and monitoring can be provided.
- It Promotes Collaboration and Communication in Business
Lack of communication can quickly ruin your business. Collaboration improves the way teams work together and solve problems. This leads to more innovation, more efficient processes, more success, and better communication. Listening to and learning from team members can help team members reach their goals.
Collaboration is also proven to foster a sense of team ownership and accountability. It also promotes transparency and knowledge sharing. Multiple teams are required to participate while using model registries. These teams are made up of both data scientists and business stakeholders. Tasks are shared among data scientists and they provide feedback.
- Faster Production of ML Models for the Business and Simplifies the ML Lifecycle
Working in a large organization with many ongoing experiments and many ML models is tedious. Managing the life cycle of ML models is often a difficult process. Model registries help address this challenge. It also helps simplify the management of the model’s life cycle.
Model registries increase the speed of production of ML models for the business and simplify the ML lifecycle. It does this by providing the following features.
- Register, track, and version your training, deployment of models in a central repository.
- It saves trained model metadata. It also runs dependencies to simplify the deployment process.
- It helps build an automated pipeline that enables continuous integration of ML models.
- It compares the model running in the production environment with the trained model.
- The ML model registry can also track and store online and offline metrics for your model. This feature allows you to easily search for models in production. It also helps identify performance degradation (or conceptual deviations).
- You can also compare online and offline performance. This is done to see which production models need to be reviewed, maintained, or archived.
- Not only can you track model metrics in both production and training. You can also track system metrics to understand. This will help understand which models consume the most application resources.
The use of ML model registries is an important component for building an efficient ML model. The model streamlines the model deployment process. It benefits a business by accelerating business operations. It ensures ML model security and governance. It also promotes collaboration and communication in business. It guarantees faster production of ML models for the business. It also simplifies the ML lifecycle.