Kubeflow is designed to package the many components used in ML applications into cloud-native components, integrate the components, and exploit all the portability and management features that Kubernetes brings to the compute environment.
- A Kubeflow package (container) comprises integrated ML components.
Features and Benefits
- Learn about the lifecycle of machine learning applications.
- Understand how using Kubernetes and containers can help improve the management of the ML application lifecycle.
Key questions answered
- Why is the lifecycle for ML different from other software applications?
- What is the role of connectors in the Kubeflow project?
Table of contents
Containerized solution for the ML application lifecycle
A Kubeflow package (container) comprises integrated ML components