Overview

Defining ML Workflows including:

• Managing data
• Running notebooks
• Training Models
• Serving Models

Creating a Notebook

From the Notebook menu, create a new notebook server.

Components

Central Dashboard

The central user interface (UI) in Kubeflow

Notebook Servers

Using Jupyter notebooks in Kubeflow

Kubeflow Pipelines

A powerful platform for building end to end ML workflows. Pipelines allow you to build a set of steps to handle everything from collecting data to serving your model.

KFServing

Kubeflow model deployment and serving toolkit

Katib

Katib is a Kubernetes-native project for automated machine learning (AutoML). Katib supports hyperparameter tuning, early stopping and neural architecture search (NAS). 

Training Operators

Training of ML models in Kubeflow through operators

Multi-Tenancy

Multi-user isolation and identity access management (IAM)