Microsoft Cognitive Services vs PyTorch

Overview

Microsoft Cognitive Services

Stacks52

Followers34

Votes0

PyTorch

Stacks1.6K

Followers1.5K

Votes43

GitHub Stars94.7K

Forks25.8K

Microsoft Cognitive Services vs PyTorch: What are the differences?

Developers describe Microsoft Cognitive Services as "*APIs, SDKs, and services available to help developers build intelligent applications *". Infuse your apps, websites and bots with intelligent algorithms to see, hear, speak, understand and interpret your user needs through natural methods of communication. Transform your business with AI today. On the other hand, PyTorch is detailed as "A deep learning framework that puts Python first". 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.

Microsoft Cognitive Services and PyTorch belong to "Machine Learning Tools" category of the tech stack.

PyTorch is an open source tool with 30.5K GitHub stars and 7.46K GitHub forks. Here's a link to PyTorch's open source repository on GitHub.

STILLWATER SUPERCOMPUTING INC, Suggestic, and cotobox are some of the popular companies that use PyTorch, whereas Microsoft Cognitive Services is used by Megtag, Universal Electronics Inc., and Daimler Fleetboard Innovation Hub. PyTorch has a broader approval, being mentioned in 28 company stacks & 165 developers stacks; compared to Microsoft Cognitive Services, which is listed in 12 company stacks and 3 developer stacks.

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Advice on Microsoft Cognitive Services, PyTorch

Adithya

Student at PES UNIVERSITY

May 11, 2020

Needs advice

I have just started learning some basic machine learning concepts. So which of the following frameworks is better to use: Keras / TensorFlow/PyTorch. I have prior knowledge in python(and even pandas), java, js and C. It would be nice if something could point out the advantages of one over the other especially in terms of resources, documentation and flexibility. Also, could someone tell me where to find the right resources or tutorials for the above frameworks? Thanks in advance, hope you are doing well!!

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Comments

Detailed Comparison

Microsoft Cognitive Services	PyTorch
Infuse your apps, websites and bots with intelligent algorithms to see, hear, speak, understand and interpret your user needs through natural methods of communication. Transform your business with AI today.	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.
Build confidently with the first AI services to achieve human parity in computer vision, speech, and language; Apply AI to more scenarios with the most comprehensive portfolio of domain-specific AI capabilities on the market; Deploy anywhere from the cloud to the edge with containers	Tensor computation (like numpy) with strong GPU acceleration;Deep Neural Networks built on a tape-based autograd system
Statistics
GitHub Stars -	GitHub Stars 94.7K
GitHub Forks -	GitHub Forks 25.8K
Stacks 52	Stacks 1.6K
Followers 34	Followers 1.5K
Votes 0	Votes 43
Pros & Cons
No community feedback yet	Pros 15 Easy to use 11 Developer Friendly 10 Easy to debug 7 Sometimes faster than TensorFlow Cons 3 Lots of code 1 It eats poop
Integrations
No integrations available	Python

What are some alternatives to Microsoft Cognitive Services, PyTorch?

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.

Keras

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

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.

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.