Earlier this month the Kubeflow community introduced Kubeflow 1.0 up from its stable and beta release of 0.7. Being one of the top contributors to Kubeflow, Cisco announced the release of Cisco Kubeflow Starter Pack. The company claims this pack will make operationalizing machine learning for large scale deployments easier.
Earlier this month the Kubeflow community introduced Kubeflow 1.0 up from its stable and beta release of 0.7. Being one of the top contributors to Kubeflow, Cisco announced the release of Cisco Kubeflow Starter Pack. The company claims this pack will make operationalizing machine learning for large scale deployments easier.
Kubeflow is an open-sourced project that offers an end-to-end machine learning (ML) stack orchestration toolkit to build on Kubernetes. Kubeflow is designed to make ML workflows easier to deploy, maintain, and coordinate on Kubernetes clusters. Some of its more well-known features include using Jupyter Notebook as its primary user interface for data scientists, machine learning engineers. Initially TensorFlow was Kubeflow’s deep learning framework. Version 1.0 supports other frameworks including PyTorch. Kubeflow has model serving. According to the Kubeflow community, there are built-in capabilities with TFServing enabling models to be used without worrying about the detailed logistics of a custom application. Along with many other features that are beyond the scope of this article.
Cisco, seeing an opportunity to help IT teams work closely with their data scientist counterparts, released Cisco Kubeflow Starter Pack. This pack is said to provides IT teams with a baseline set of tools to get started with Kubeflow.
The Cisco Kubeflow Starter Pack includes:
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