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.
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.
- Read our Code of conduct.
- Contribute and report bugs.
- Contribute to this documentation.
- Join the Discourse forum.
- Talk to us on Matrix.
- Try out some projects.
Last updated a day ago.