Get started with machine learning tooling using Charmed Kubeflow. Depending on your experience and interests, there are various examples that you could try out, including data drift, autoML or AI at the edge. Reefer to Charmed Kubeflow documentation if you would like to deploy it.
This repository is a living library for examples that use and integrate a wide range of tooling, frameworks and libraries from the AI work such as Kubeflow, MLFlow, Spark, Seldon, Triton or H2O. Its main objective is to enable professionals, data scientists and engineer, to deepen their machine learning knowledge and get familiar with open source. It is a community initiative, where everyone can contribute with their examples, share their feedback and learn more about machine learning, MLOps and more..
Maintained Examples are expected to be updated with every Kubeflow release. to use the latest and greatest features, current guidelines and best practices, and to refresh command syntax, output, changed prerequisites, as needed.
| Name | Description | Complexity leve |
|---|---|---|
| End-to-end MLOps pipeline | Run end-to-end MLOps pipeline with Charmed Kubeflow, MLFlow & Minio | Intermediate |
| AutoML with H2O | Try AutoML capabilities with H20 image using Jupyter Notebooks within Charmed Kubeflow | Intermediate |
| Notebook Integrations | Get started with AI using Notebook images | Beginner |
| Talking Jellyfish | Learn how using multiple models to solve the business case. | Beginner |
MLOps is the short term for machine learning operations and it represents a set of practices that aim to simplify workflow processes and automate machine learning and deep learning deployments. It accomplishes the deployment and maintenance of models reliably and efficiently for production, at a large scale.
Kubeflow is the open source machine learning toolkit on top of Kubernetes.
Charmed Kubeflow is Canonical's official distribution of the upstream project.Using it, data scientists and machine learning engineers benefit from having ML deployments that are simple, portable and scalable. It has capabilities that cover a wide range of tasks, from experimentation using Notebooks, to training using Kubeflow Pipelines or tuning, using Katib.
Learn more about Charmed Kubeflow
- Stay up to date reading Ubuntu AI on Medium
- Read our guide to MLOps
- Share your feedback with us
- Use Charmed Kubeflow docs