The Canonical Charmed Kubeflow team is releasing Charmed Kubeflow 1.4—a state-of-the-art MLOps platform. 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 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, according to the vendor.
The latest release adds several features for advanced model lifecycle management, including upstream Kubeflow 1.4 and support for MLFlow integration.
Data scientists and data engineers can use the MLFlow integration capability to build automatic model drift detection and trigger a Kubeflow model retraining pipeline. Model drift occurs as model accuracy starts to decline over time due to changes in the live prediction dataset versus the training dataset.
Enabling MLFlow on a Kubernetes cluster and integrating it with a Charmed Kubeflow deployment using the Juju unified operator framework is straightforward, and the MLFlow Juju operator is available in CharmHub for immediate deployment.
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.
The authentication provider integration guide provides more information on setting up multi-user access controls for the Charmed Kubeflow 1.4 MLOps platform.
The new release is in the CharmHub stable channel now, and can be deployed to any conformant Kubernetes cluster using a single Juju command.
For more information about this news, visit https://canonical.com/.