Congratulations to the community of Kubeflow users, contributors, community evangelists and corporate sponsors who helped make the new Kubeflow 1.4 re

Kubeflow 1.4 Contributions Summary

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2021-10-20 16:00:16

Congratulations to the community of Kubeflow users, contributors, community evangelists and corporate sponsors who helped make the new Kubeflow 1.4 release possible!

The Kubeflow 1.4 release lays several important building blocks for the use of advanced metadata workflows. A quick summary of 1.4’s top deliveries includes:

Kubeflow 1.4 enables the use of metadata in advanced machine learning (ML) workflows, especially in the Kubeflow Pipelines SDK. With the Pipelines SDK and its new V2-compatible mode, users can create advanced ML pipelines with Python functions that use the MLMD as input/output arguments. This simplifies metrics visualization.

Another enhancement to Pipelines is the option to use the Emissary executor for non-Docker Kubernetes container runtime requirements. In addition, 1.4 can support metadata-based workflows to streamline the creation of TensorBoard visualizations and to serve ML models.

For folks not familiar with the Kubeflow project, it is made up of multiple technologies that when combined deliver a machine learning platform. For the sake of manageability, the Kubeflow project has been broken down into six working groups with associated GitHub repositories. This makes it easier to develop, document and release specific building blocks of functionality.