Charmed Kubeflow documentation

Charmed Kubeflow (CKF) is an open-source, end-to-end, production-ready MLOps platform on top of cloud-native technologies.

It translates Machine Learning (ML) steps into complete workflows, including training, tuning, and shipping of ML models. It enables automation of workflows, increases quality of models, and simplifies deployment of ML workloads into production in a reliable way.

CKF meets the need of building ML applications in a structured and consistent manner while contributing to higher productivity and better collaboration within teams.

It is intended for data scientists and ML engineers, providing an advanced toolkit to organise and scale their work.

Canonical also offers a fully managed solution of CKF running on top of Microsoft Azure. It provides supported operation management, including deployment, as well as dedicated customer service. Get started here.


In this documentation

Tutorial
Get started - a hands-on introduction to CKF for newcomers
How-to guides
Step-by-step guides covering key operations and common tasks with CKF
Explanation
Discussion and clarification of key topics
Reference
Technical information, including specifications, APIs, settings and configuration

Project and community

Charmed Kubeflow is a member of the Ubuntu family. It’s an open-source project that welcomes community contributions, suggestions, fixes and constructive feedback.


Last updated 3 days ago.