End-to-end Machine Learning using Kubeflow-Reference-Cited by-同舟云学术

End-to-end Machine Learning using Kubeflow

Published:2022-01-08 Issue: Volume: Page:
ISSN:
Container-title:5th Joint International Conference on Data Science & Management of Data (9th ACM IKDD CODS and 27th COMAD)
language:
Short-container-title:

Author:

George Johnu¹,Saha Amit²

Affiliation:

1. Nutanix, IN

2. Cisco Systems, IN

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3493700.3493768

Reference19 articles.

1. [ 1 ] Amazon Elastic Kubernetes Service 2021. https://aws.amazon.com/eks/ [1] Amazon Elastic Kubernetes Service 2021. https://aws.amazon.com/eks/

2. [ 2 ] Azure Kubernetes Service 2021. https://azure.microsoft.com/en-us/services/kubernetes-service/ [2] Azure Kubernetes Service 2021. https://azure.microsoft.com/en-us/services/kubernetes-service/

3. [ 3 ] Cisco Kubeflow Starter Pack 2020. https://github.com/CiscoAI/cisco-kubeflow-starter-pack [3] Cisco Kubeflow Starter Pack 2020. https://github.com/CiscoAI/cisco-kubeflow-starter-pack

4. [ 4 ] Cloud Native Computing Foundation 2021. https://www.cncf.io/ [4] Cloud Native Computing Foundation 2021. https://www.cncf.io/

5. Johnu George Ce Gao Richard Liu Hou Gang Liu Yuan Tang Ramdoot Pydipaty and Amit Kumar Saha. 2020. A Scalable and Cloud-Native Hyperparameter Tuning System. CoRR abs/2006.02085(2020). arxiv:2006.02085https://arxiv.org/abs/2006.02085 Johnu George Ce Gao Richard Liu Hou Gang Liu Yuan Tang Ramdoot Pydipaty and Amit Kumar Saha. 2020. A Scalable and Cloud-Native Hyperparameter Tuning System. CoRR abs/2006.02085(2020). arxiv:2006.02085https://arxiv.org/abs/2006.02085

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