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Revision as of 17:23, 29 December 2019
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| Developer(s) | |
|---|---|
| Initial release | March 28, 2018 |
| Stable release | 0.7[1]
/ May 7, 2019 |
| Repository | github |
| Platform | Linux, Windows, MacOS |
| License | Apache License 2.0 |
| Website | www |
 | The following Wikipedia contributor has declared a personal or professional connection to the subject of this article. Relevant policies and guidelines may include conflict of interest, autobiography, and neutral point of view. |
Kubeflow is a free and open-source machine learning platform co-founded by David Aronchick, Jeremy Lewi and Vishnu Kannan, built by developers at Google, Cisco, RedHat, CoreOS and CaiCloud, and first released at Kubecon North America in 2017[2]. Kubeflow is designed to enable using machine learning pipelines to orchestrate complicated workflows running on Kubernetes (e.g. doing data processing then using TensorFlow or PyTorch to train a model, and deploying to TensorFlow Serving). Kubeflow was based on Google's internal method to deploy TensorFlow models to Kubernetes called TensorFlow Extended.[3]
References
- ^ GitHub - kubeflow/kubeflow: Machine Learning Toolkit for Kubernetes., Kubeflow, 2019-06-18, retrieved 2019-06-18
- ^ Hot Dogs or Not" - At Scale with Kubernetes [I] - Vish Kannan & David Aronchick, Google, retrieved 2019-12-20
- ^ "Kubeflow". Kubeflow. Retrieved 2019-06-18.