Kubeflow and MLflow are prominent open-source platforms in the machine learning operations (MLOps) space, each serving distinct roles in the machine learning lifecycle. Kubeflow, developed by Google, is a Kubernetes-based orchestration toolkit that focuses on deploying, scaling, and managing large-scale machine learning systems, offering features like pipelines, KFServing, and training operators for efficient model training and deployment. In contrast, MLflow, supported by Databricks, specializes in tracking machine learning experiments and managing model lifecycles with features like experiment tracking, model registry, and project packaging. While both platforms facilitate scalable and customizable ML environments with strong third-party support, Kubeflow is better suited for complex infrastructure and orchestration needs, ideal for larger teams, whereas MLflow is more geared towards individual data scientists focusing on organizing their experiments and models. Both platforms are complemented by third-party solutions like JFrog ML, which provide managed services to streamline MLOps processes and offer enhanced infrastructure capabilities.