Production model governance is the framework that organizations use to manage the rules and controls over machine learning models. Some of these controls include access control, validation, tracing, and changelogs.
Access controls allow IT teams to maintain control over production models, and limitations should be implemented at the individual level or based on an employee’s role. The goal is to limit the number of people who can change the machine learning model in the production environment.
For those who do have access to change models that are already in production, change and audit logs must be kept to track changes and note who performed them.
Organizations can scale AI platforms and models with production model governance in place, and it also makes it easier to complete legal and compliance reports. Having this framework in place is essential, as the results of machine learning models drive decisions that can have significant impacts on customers, employees, and investors.
An essential step in this process is to establish the roles and responsibilities within the production model lifecycle. Which team will manage the design, validation, or maintenance of the model? These are essential roles that should be clearly defined and include qualifications and training requirements.
Another critical component of production model governance is machine learning operations or MLOps. This is the collaboration between the departmental managers and data scientists that operate the models to ensure that the governance framework is implemented effectively.
Production model government is essential to ensuring that your machine learning models are still useful. It allows organizations to monitor performance and review data benchmarking to see if the model needs to be retrained or reengineered.
As models change over time, these enhancements are recorded in the model history and changelogs as part of compliance regulations. An efficient production model governance framework facilitates this process and ensures that there is a clear audit trail available for inspection.
Having these processes in place also helps to minimize legal and operational risks, and it facilitates troubleshooting so that issues can be resolved quickly. This ensures that there is trust in the AI platforms and models that your business relies on to make data-driven decisions.
Putting governance measures in place will also help your organization be ready for changes that occur throughout your industry. If you have change processes in place, you can quickly adapt your models to improve results as the data you are working with evolves.
At LogicPlum, we provide the tools you need to not only build successful machine learning models but also to implement a governance framework to support the production model lifecycle. We offer MLOps as part of our AI platform, and our simplified tools will help you seamlessly utilize and maintain your automated machine learning models.