Simplify and streamline MLOps

Machine Learning Operations (MLOps), a practice of putting machine learning into industrial production, is a product of the intersection of machine learning and DevOPs in the software world, so it is similar in many ways to DevOps in 2012. When DevOps went live in 2012, many enterprises realized its value, but they struggled to implement DevOps, the toolchain was complex, and the ecosystem was not perfect. MLOps is more complex, and its packages include everything from installation, configuring training, inference infrastructure, configuring feature stores, configuring model registries, monitoring model decay, and detecting model drift. Its sheer size of the software package also makes MLOps more difficult to deploy than DevOps.

MLOps is one of the concepts incorporated into cloud-based ML platforms, including Amazon SageMaker for Amazon Web Services, Azure ML, and Google's Vertex AI. However, it has these capabilities that cannot be used in both hybrid and edge computing environments. As a result, monitoring environmental models for edge computing is proving to be a significant challenge for enterprises. When dealing with computer vision systems and interactive AI systems, it becomes more challenging to create a model of monitoring edge computing that serves them.

As open source projects like Kubeflow and MLflow mature, MLOps are actually readily available. In the coming years, we may see a streamlined and simplified approach to MLOps across cloud and edge computing environments.