ANALYSIS OF THE APPLICATION OF KUBEFLOW AS AN AI/ML EXPERIMENTATION PLATFORM IN A CLOUD NATIVE ENVIRONMENT - Agustin Putri, Dwina
This research aims to analyze the application of Kubeflow as an AI/ML experimentation platform in a cloud native environment, focusing on the ease of use of the interface and the multi-user feature for collaborative experiment access. The background of this research is based on the need for a platform that can facilitate AI/ML experiments efficiently and effectively in a dynamic and scalable environment. With the rapid advancement of AI/ML technology, there is an urgent need for a platform that can integrate various stages of experiments, from model development, testing, to model management. Built on Kubernetes, Kubeflow offers solutions with features such as notebooks for developing AI/ML models, Kubeflow Pipelines for tracking experiments, and integrated model management, thereby simplifying the complexities of AI/ML model development. This research uses an experimental method involving the configuration and testing of various Kubeflow features and comparing them with the MLflow platform. The primary focus of this research is to test the ease of use and