Amazon Personalize vs PyTorch

Overview

PyTorch

Stacks1.6K

Followers1.5K

Votes43

GitHub Stars94.7K

Forks25.8K

Amazon Personalize

Stacks20

Followers62

Votes0

Amazon Personalize vs PyTorch: What are the differences?

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; PyTorch: 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.

Amazon Personalize and PyTorch are primarily classified as "Machine Learning as a Service" and "Machine Learning" tools respectively.

PyTorch is an open source tool with 29.6K GitHub stars and 7.19K GitHub forks. Here's a link to PyTorch's open source repository on GitHub.

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Advice on PyTorch, Amazon Personalize

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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Detailed Comparison

PyTorch	Amazon Personalize
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.	Machine learning service that makes it easy for developers to add individualized recommendations to customers using their applications.
Tensor computation (like numpy) with strong GPU acceleration;Deep Neural Networks built on a tape-based autograd system	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 94.7K	GitHub Stars -
GitHub Forks 25.8K	GitHub Forks -
Stacks 1.6K	Stacks 20
Followers 1.5K	Followers 62
Votes 43	Votes 0
Pros & Cons
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	No community feedback yet
Integrations
Python	No integrations available

What are some alternatives to PyTorch, 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.

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.

MLflow

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

Related Comparisons

PyTorch is an open source tool with 29.6K GitHub stars and 7.19K GitHub forks. Here's a link to PyTorch's open source repository on GitHub.

Amazon Personalize vs PyTorch