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Kubeflow vs TensorFlow.js: What are the differences?
Developers describe Kubeflow as "Machine Learning Toolkit for Kubernetes". The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions. On the other hand, TensorFlow.js is detailed as "Machine Learning in JavaScript". Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API.
Kubeflow and TensorFlow.js can be categorized as "Machine Learning" tools.
Kubeflow and TensorFlow.js are both open source tools. It seems that TensorFlow.js with 11.2K GitHub stars and 816 forks on GitHub has more adoption than Kubeflow with 7.04K GitHub stars and 1.03K GitHub forks.
Pros of Kubeflow
- System designer9
- Google backed3
- Customisation3
- Kfp dsl3
Pros of TensorFlow.js
- Open Source6
- NodeJS Powered5
- Deploy python ML model directly into javascript2
- Cost - no server needed for inference1
- Privacy - no data sent to server1
- Runs Client Side on device1
- Can run TFJS on backend, frontend, react native, + IOT1
- Easy to share and use - get more eyes on your research1
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What is Kubeflow?
What is TensorFlow.js?
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