Kubeflow vs Microsoft Cognitive Toolkit

Overview

Microsoft Cognitive Toolkit

Stacks18

Followers21

Votes0

GitHub Stars17.2K

Forks4.4K

Kubeflow

Stacks205

Followers585

Votes18

Kubeflow vs Microsoft Cognitive Toolkit: What are the differences?

What is Kubeflow? 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.

What is Microsoft Cognitive Toolkit? An open-source toolkit for deep learning. It is an open-source toolkit for commercial-grade distributed deep learning. It describes neural networks as a series of computational steps via a directed graph.

Kubeflow and Microsoft Cognitive Toolkit belong to "Machine Learning Tools" category of the tech stack.

Kubeflow and Microsoft Cognitive Toolkit are both open source tools. It seems that Microsoft Cognitive Toolkit with 16.3K GitHub stars and 4.34K forks on GitHub has more adoption than Kubeflow with 7.23K GitHub stars and 1.08K GitHub forks.

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Detailed Comparison

Microsoft Cognitive Toolkit	Kubeflow
It is an open-source toolkit for commercial-grade distributed deep learning. It describes neural networks as a series of computational steps via a directed graph.	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.
Speed & Scalability; Commercial-Grade Quality; Easy-to-use architecture	-
Statistics
GitHub Stars 17.2K	GitHub Stars -
GitHub Forks 4.4K	GitHub Forks -
Stacks 18	Stacks 205
Followers 21	Followers 585
Votes 0	Votes 18
Pros & Cons
No community feedback yet	Pros 9 System designer 3 Customisation 3 Kfp dsl 3 Google backed 0 Azure
Integrations
C++ Python	Kubernetes Jupyter TensorFlow

What are some alternatives to Microsoft Cognitive Toolkit, Kubeflow?

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.

scikit-learn

scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

PyTorch

PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.

Keras

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/

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

Polyaxon

An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.

Streamlit

It is the app framework specifically for Machine Learning and Data Science teams. You can rapidly build the tools you need. Build apps in a dozen lines of Python with a simple API.

MLflow

MLflow is an open source platform for managing the end-to-end machine learning lifecycle.

H2O

H2O.ai is the maker behind H2O, the leading open source machine learning platform for smarter applications and data products. H2O operationalizes data science by developing and deploying algorithms and models for R, Python and the Sparkling Water API for Spark.

PredictionIO

PredictionIO is an open source machine learning server for software developers to create predictive features, such as personalization, recommendation and content discovery.