Introduction to Kubeflow (en)

The course ‘Introduction to Kubeflow’ provides a comprehensive guide on installing, utilizing, and customizing Kubeflow, an open-source framework designed to simplify the development, training, and deployment of machine learning models on Kubernetes. Throughout the course, students will be introduced to the basic concepts of Kubeflow and the installation process in a Kubernetes cluster. Subsequently, they will be guided through interacting with Kubeflow’s Notebook server for the development and execution of machine learning models. The course will also cover the creation of custom images for running models on Kubeflow, as well as the use of KServe for model deployment in production environments.

Additionally, students will explore the Discovery Pipeline and experiments for managing the machine learning workflow and optimizing hyperparameters. The course will culminate with a practical demonstration on customizing the Kubeflow dashboard and managing the entire ML and MLOps lifecycle. In summary, ‘Introduction to Kubeflow’ offers students an in-depth overview of Kubeflow’s capabilities and functionalities, preparing them to utilize this powerful tool in implementing and managing machine learning projects on Kubernetes.

Category: Artificial Intelligence