As a data scientist or a machine learning engineer, you have probably heard about Kubeflow and MLflow. These are the two most popular open-source tools under the machine learning platforms umbrella. Because these platforms are the open-source category leaders, they are often compared against each other despite being quite different.
Both products today offer a rather extensive set of capabilities for developing and deploying machine learning models. However, the products started from very different perspectives, with Kubeflow being more orchestration and pipeline-focused and MLflow being more experiment tracking-focused.
Kubeflow offers a scalable way to train and deploy models on Kubernetes. It is an orchestration medium that enables a cloud application framework to operate smoothly. Some of the components of Kubeflow include the following:
Notebooks: It offers services for creating and managing interactive Jupyter notebooks in corporate settings. Also included is the ability for users to build notebook containers or pods directly in clusters.