Arrikto: ML Model Deployments on Kubernetes Can Get Better - The New Stack

Data scientists and engineers typically work in parallel to DevOps production pipelines when they create machine learning (ML) models. Through so-called MLOps, the ML models these teams build are often then integrated into the application-deployment environment once ready for production. These data scientists or MLOps teams might also rely on GitOps to serve as the immutable application structure with a Git repository so that the ML models are distributed across different environments, such as hybrid clouds. To facilitate the deployment of ML models and applications on Kubernetes, open source Kubeflow has emerged as one of the more promising open source tools to help with the process. Still, the challenges associated with MLOps teams deploying ML models and applications on Kubernetes -- even with tools such as Kubeflow -- are fraught with difficulty and complexity.