Organizations are increasingly investing in MLOps to boost productivity and create advanced machine learning models, leading to a surge in new technologies and tools for managing tasks and data pipelines. Choosing the right platform for automated workflows is critical, with options like Kubeflow and Argo being popular choices. Kubeflow is a Kubernetes-based ML orchestration toolkit that offers a comprehensive suite for deploying, scaling, and managing large-scale systems, while Argo is a container-native workflow engine designed for orchestrating parallel jobs on Kubernetes. Both platforms share similarities, such as being open-source and leveraging Kubernetes for pipeline orchestration, but Kubeflow provides additional ML-specific features, making it a more centralized solution for the entire model lifecycle. Conversely, Argo is focused purely on pipeline orchestration, suitable for any Directed Acyclic Graph (DAG) workflows. Teams may choose between the two based on their specific needs for ML capabilities and workflow orchestration. For those seeking an alternative, JFrog ML offers a managed MLOps platform that simplifies maintenance and setup, providing robust features and cloud-based scalability for transforming models into well-engineered products.