Amazon Personalize vs Polyaxon

Overview

Polyaxon

Stacks11

Followers65

Votes14

GitHub Stars3.7K

Forks325

Amazon Personalize

Stacks20

Followers62

Votes0

Amazon Personalize vs Polyaxon: What are the differences?

Developers describe Amazon Personalize as "Real-time personalization and recommendation". Machine learning service that makes it easy for developers to add individualized recommendations to customers using their applications. On the other hand, Polyaxon is detailed as "An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications". An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.

Amazon Personalize belongs to "Machine Learning as a Service" category of the tech stack, while Polyaxon can be primarily classified under "Machine Learning Tools".

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Detailed Comparison

Polyaxon	Amazon Personalize
An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.	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 3.7K	GitHub Stars -
GitHub Forks 325	GitHub Forks -
Stacks 11	Stacks 20
Followers 65	Followers 62
Votes 14	Votes 0
Pros & Cons
Pros 2 API 2 Python Client 2 Notebook integration 2 Tensorboard integration 2 Streamlit integration	No community feedback yet
Integrations
Docker Kubernetes Helm Python Jupyter Caffe2 TensorFlow Keras Gluon	No integrations available

What are some alternatives to Polyaxon, 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

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.