Machine learning operations, or MLOps, refers to the technology and processes that allow machine learning models to be deployed and scaled within an organization. It provides the governance needed to ensure that the deployment of machine learning models is successful.
Since data scientists can use various modeling languages and frameworks, it can delay model deployment if the existing enterprise systems do not work with the model seamlessly. MLOps allows teams to deploy models quickly regardless of how they are built.
MLOps also provides tools for monitoring your machine learning models – an essential resource for detecting data drift and managing metrics unique to your model. These tools also allow for models to be tested and updated throughout the machine learning lifecycle, minimizing interruptions to service and business processes.
As far as governance is concerned, MLOps is another essential component. It offers access control and tracking capabilities so that audit trails can be provided. These tools minimize risks associated with model deployment and help to ensure compliance with regulatory requirements.
MLOps is crucial because it facilitates the machine learning lifecycle and allows the models to be successful in the production environment.
In other words, the machine learning operations provide the framework for model deployment, management, monitoring, and governance – each of these must be performed to scale machine learning models and to allow the business to automate processes.
Another unique benefit of MLOps is that it allows data scientists to focus on developing the algorithms. Rather than taking responsibility for managing the model after it has been deployed, ownership can instead be shifted to the MLOps engineers.
It doesn’t matter if you only have BI analysts, IT leaders, or executives wanting to leverage MLOps 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 MLOps.
The LogicPlum MLOps platform can be used with machine learning models built with any modern language – Java, Python, R, and many others. We include pre-constructed environments for frameworks and a wide variety of tools to help your business take ownership of the machine learning lifecycle.
At LogicPlum, we understand that using the insights gained from our models is essential for your business to make data-driven decisions, so our tools will allow you to update and maintain models without interrupting service.
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