The new release enhances how enterprises collaborate on AI and machine learning innovation throughout the lifecycle of the project.

This week the Canonical, the U.K.-based publisher of Ubuntu, announced the release of Charmed Kubeflow 1.4. The new release enables data science teams to securely collaborate on AI/ML innovation on any cloud, from concept to production.

Charmed Kubeflow is free to use, the comopany says. Developers can deploy the solution in any environment without constraints, paywall or restricted features.

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.

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, according to Canonical.

Delivering better lifecycle management

The latest release adds several features for advanced model lifecycle management, including upstream Kubeflow 1.4 and support for MLFlow integration.

Data scientists can start immediately with Charmed Kubeflow 1.4 using Juju. This is the unified operator framework for hyper-automated management of applications running on both virtual machines and Kubernetes. Users can deploy the new release to any conformant Kubernetes cluster using a single Juju command.

Kubeflow 1.4 comes with major usability improvements over previous releases, including a unified training operator. The new training operator supports the popular AI/ML frameworks TensorFlow, MXNet, XGBoost and PyTorch. This greatly simplifies the solution, improving future extensibility and consuming fewer resources on the Kubernetes cluster.

Cononical has also added support for MLFlow integration to the Charmed Kubeflow solution. This enables automated model lifecycle management using MLFlow metrics and the MLFlow model registry, according to Canonical.

Better governance with improved multi-user support

Charmed Kubeflow 1.4 fully supports multi-user deployment scenarios out of the box for all Kubeflow components, including Kubeflow notebooks, pipelines, and experiments. This update simplifies using Charmed Kubeflow to improve governance and reduce the occurrence of shadow-IT environments, whilst helping to combat organisational data leakage.