Kubeflow

116
375
+ 1
12
TensorFlow.js

118
261
+ 1
9
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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
Pros of TensorFlow.js

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What is Kubeflow?

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.

What is TensorFlow.js?

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
What companies use Kubeflow?
What companies use TensorFlow.js?

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What tools integrate with Kubeflow?
What tools integrate with TensorFlow.js?

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What are some alternatives to Kubeflow and TensorFlow.js?
TensorFlow
TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
Apache Spark
Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
MLflow
MLflow is an open source platform for managing the end-to-end machine learning lifecycle.
Airflow
Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed.
Polyaxon
An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.
See all alternatives
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