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 such as Tensorflow and Pytorch.
Multi-Tenancy
Multi-user isolation and identity access management (IAM)
KFServing
Jupyter Notebooks
Defacto standard for data scientists for performing rapid data analysis.
Kubeflow Pipelines
A pipeline component is one step in the workflow that performs one specific task.
Takes inputs and produces outputs.
Hyperparameter Tuning with Katib
Reference | URL |
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Kubeflow 101 Videos | https://www.youtube.com/playlist?list=PLIivdWyY5sqLS4lN75RPDEyBgTro_YX7x |