Microsoft Cognitive Toolkit vs Amazon Personalize

Overview

Microsoft Cognitive Toolkit

Stacks18

Followers21

Votes0

GitHub Stars17.2K

Forks4.4K

Amazon Personalize

Stacks20

Followers62

Votes0

Amazon Personalize vs Microsoft Cognitive Toolkit: What are the differences?

What is Amazon Personalize? Real-time personalization and recommendation. Machine learning service that makes it easy for developers to add individualized recommendations to customers using their applications.

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.

Amazon Personalize and Microsoft Cognitive Toolkit are primarily classified as "Machine Learning as a Service" and "Machine Learning" tools respectively.

Some of the features offered by Amazon Personalize are:

Combine customer and contextual data to generate high-quality recommendations
Automated machine learning
Continuous learning to improve performance

On the other hand, Microsoft Cognitive Toolkit provides the following key features:

Speed & Scalability
Commercial-Grade Quality
Easy-to-use architecture

Microsoft Cognitive Toolkit is an open source tool with 16.3K GitHub stars and 4.34K GitHub forks. Here's a link to Microsoft Cognitive Toolkit's open source repository on GitHub.

🔥 Trending in Development & Training Tools on StackShare

Tinker AI Development Training Tools

Tinker

Try it View Docs Alternatives

Try

0

1

Detailed Comparison

Microsoft Cognitive Toolkit	Amazon Personalize
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.	Machine learning service that makes it easy for developers to add individualized recommendations to customers using their applications.
Speed & Scalability; Commercial-Grade Quality; Easy-to-use architecture	Combine customer and contextual data to generate high-quality recommendations; Automated machine learning; Continuous learning to improve performance; Bring your own algorithms; Easily integrate with your existing tools;
Statistics
GitHub Stars 17.2K	GitHub Stars -
GitHub Forks 4.4K	GitHub Forks -
Stacks 18	Stacks 20
Followers 21	Followers 62
Votes 0	Votes 0
Integrations
C++ Python	No integrations available

What are some alternatives to Microsoft Cognitive Toolkit, Amazon Personalize?

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/

NanoNets

Build a custom machine learning model without expertise or large amount of data. Just go to nanonets, upload images, wait for few minutes and integrate nanonets API to your application.

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.

Inferrd

It is the easiest way to deploy Machine Learning models. Start deploying Tensorflow, Scikit, Keras and spaCy straight from your notebook with just one extra line.