Production model lifecycle management refers to the process of maintaining and updating automated machine learning models and AI platforms. Although deploying the model is an essential step, the lifecycle of the system can be complicated and includes several other phases.
The production model lifecycle includes managing all aspects of the machine learning model, including its development, testing, launch, ongoing support, and any future enhancements that are required.
In the design stage, you are working towards creating the concept of a model that will help you achieve your business goal. Next, you must build the model – this involves using clean data to ensure that it works as desired.
The model must be tested in a separate workspace to ensure that it is accurate, and then you must perform user acceptance. When the model is performing up to standards, you can deploy it to end-users.
The process does not end here – as your business grows and changes, your model may need to be retrained with newer data. This does not alter the fundamental algorithm it is built on, but instead allows the model to incorporate new patterns and trends that occur in the updated data.
Each iteration of a model must be re-tested before being deployed, and if a new model is developed it must be compared to the previous one to be sure that it does perform better.
MLOps, short for machine learning operations, is another way to describe the collaboration that occurs between data scientists and other professionals within an organization to facilitate production model lifecycle management.
To ensure your AI platform is working as it should be, and that it is still valid after some time has passed, your organization must implement the steps in the model lifecycle management process.
Properly testing and enhancing your models certifies that the predictions made are accurate and performing correctly. You are at risk of making incorrect business decisions if your model is producing inaccurate results or is not performing as expected, so you must have an MLOps system in place.
The management of these models also allows your IT team to identify areas where your models can be enhanced, thus providing more excellent opportunities for your business to gain additional insights from the data it collects.
It doesn’t matter if you only have BI analysts, IT leaders, or executives wanting to leverage production model lifecycle management or if you have a team of in-house data scientists looking to be more impactful with their analytic output. Any organization looking to start or advance their AI journey can benefit from production model lifecycle management.
Production model lifecycle management is an essential aspect of running effective machine learning models that help your business make data-driven decisions. LogicPlum provides all the tools necessary to ensure that you not only make the most of your machine learning models but also can effectively test and enhance them once they are deployed in your organization.
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