Kubeflow, developed by Google, and Amazon SageMaker, launched by Amazon in 2017, are prominent MLOps platforms designed to facilitate the development, training, and deployment of machine learning models, each with distinct focuses and offerings. Kubeflow is an open-source toolkit that operates on Kubernetes, emphasizing orchestration and pipelines to manage ML workflows, making it suitable for those familiar with Kubernetes environments seeking a free and customizable solution. In contrast, Amazon SageMaker provides a managed cloud service with comprehensive tools for data preparation, model training, and deployment, offering features like an integrated IDE, Feature Store, and Data Wrangler, which cater to teams already embedded in the AWS ecosystem and willing to incur associated costs. Both platforms support the full ML lifecycle and accommodate common Python-based ML frameworks, but their differences in user experience, pricing, and integration capabilities can influence an organization's choice between the two.