Amazon Personalize vs TensorFlow.js

Overview

TensorFlow.js

Stacks184

Followers378

Votes18

GitHub Stars19.0K

Forks2.0K

Amazon Personalize

Stacks20

Followers62

Votes0

Amazon Personalize vs TensorFlow.js: 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 TensorFlow.js? 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.

Amazon Personalize can be classified as a tool in the "Machine Learning as a Service" category, while TensorFlow.js is grouped under "Machine Learning Tools".

TensorFlow.js is an open source tool with 11.2K GitHub stars and 818 GitHub forks. Here's a link to TensorFlow.js's open source repository on GitHub.

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Detailed Comparison

TensorFlow.js	Amazon Personalize
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	Machine learning service that makes it easy for developers to add individualized recommendations to customers using their applications.
-	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 19.0K	GitHub Stars -
GitHub Forks 2.0K	GitHub Forks -
Stacks 184	Stacks 20
Followers 378	Followers 62
Votes 18	Votes 0
Pros & Cons
Pros 6 Open Source 5 NodeJS Powered 2 Deploy python ML model directly into javascript 1 Cost - no server needed for inference 1 Easy to share and use - get more eyes on your research	No community feedback yet
Integrations
JavaScript TensorFlow	No integrations available

What are some alternatives to TensorFlow.js, 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.

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.