Charmed Kubeflow - AI/MLOps on any cloud

Looking for a fully supported platform for MLOps?

Canonical Charmed Kubeflow is a state of the art, fully supported MLOps platform that helps data scientists collaborate on AI innovation on any cloud from concept to production, offered by Canonical - the open source experts.

Kubeflow diagram

Charmed Kubeflow is free to use: the solution can be deployed in any environment without constraints, paywall or restricted features. Data labs and MLOps teams only need to train their data scientists and engineers once to work consistently and efficiently on any cloud – or on-premise.

Charmed Kubeflow offers a centralised, browser-based MLOps platform that runs on any conformant Kubernetes – offering enhanced productivity, improved governance and reducing the risks associated with shadow IT.

Learn more about deploying and using Charmed Kubeflow at https://charmed-kubeflow.io.

Key features

  • Centralised, browser-based data science workspaces: familiar experience
  • Multi user: one environment for your whole data science team
  • NVIDIA GPU support: accelerate deep learning model training
  • Apache Spark integration: empower big data driven model training
  • Ideation to production: automate model training & deployment
  • AutoML: hyperparameter tuning, architecture search
  • Composable: edge deployment configurations available

What’s included in Charmed Kubeflow 1.4

  • LDAP Authentication
  • Jupyter Notebooks
  • Work with Python and R
  • Support for TensorFlow, Pytorch, MXNet, XGBoost
  • TFServing, Seldon-Core
  • Katib (autoML)
  • Apache Spark
  • Argo Workflows
  • Kubeflow Pipelines

Scale AI experiments to thousands of jobs

Founded on Kubernetes, scaling machine learning with Charmed Kubeflow is painless. Multi-cloud Charmed Kubeflow delivers the elasticity of the public clouds with the governance of on-premise deployment. Train your team once to work anywhere.

Why engineers and data scientists choose Charmed Kubeflow

Quickstart guide

Charmed Kubeflow delivers a powerful, sophisticated end-to-end MLOps platform which you can deploy in half an hour or less, using MicroK8s or another conformant Kubernetes distribution. Read the quickstart guide to get up and running.

Now with AutoML – introducing Katib

Charmed Kubeflow includes Katib to simplify model hyperparameter tuning and neural architecture search experiments. Find the best ML model faster with Charmed Kubeflow and Katib.

Documentation

Please see the official docs site for complete documentation of the Charmed Kubeflow distribution.

All–you–need support services

Enterprise support, deployment and fully managed Charmed Kubeflow are available. Rely on 24/7 full-stack enterprise support with the SLAs you need. Offload deployment and management to Canonical’s engineers.

Contact us to find out more about our support, expert services and managed services offers for Charmed Kubeflow.

An ecosystem for MLOps

Charmed Kubeflow includes support for integration with a growing ecosystem of complementary open source tools including MLFlow, Seldon Core and Apache Spark, enabling you to get even more productive with Kubeflow.

Navigation

Level Path Navlink
1 charmed-kubeflow Introduction
1 quickstart Quickstart guide
1 remote-access Configure remote access
1 ldap Configure OpenLDAP authentication
1 oidc-auth OIDC authentication with Keycloak
1 authorisation Understanding authorisation
1 spot-instances Using AKS spot instances
1 kubeflow-basics Kubeflow basics
1 visual-workflows Visual workflow design
1 mlops-pipelines-with-kubeflow-mlflow-and-seldon-core Building MLOps Pipelines
1 gpu Using GPUs
1 fpga-katib InAccel FPGA Operator with Katib
1 vscode Using VSCode
1 install Install guide
1 troubleshooting Troubleshooting
1 uninstall Uninstall
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