Amazon Personalize vs Neptune

Overview

Amazon Personalize

Stacks20

Followers62

Votes0

Neptune

Stacks16

Followers38

Votes2

Amazon Personalize vs Neptune: 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, Neptune is detailed as "The most lightweight experiment tracking tool for machine learning". It brings organization and collaboration to data science projects. All the experiement-related objects are backed-up and organized ready to be analyzed, reproduced and shared with others. Works with all common technologies and integrates with other tools.

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

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, Neptune provides the following key features:

Experiment tracking
Experiment versioning
Experiment comparison

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Detailed Comparison

Amazon Personalize	Neptune
Machine learning service that makes it easy for developers to add individualized recommendations to customers using their applications.	It brings organization and collaboration to data science projects. All the experiement-related objects are backed-up and organized ready to be analyzed, reproduced and shared with others. Works with all common technologies and integrates with other tools.
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;	Experiment tracking; Experiment versioning; Experiment comparison; Experiment monitoring; Experiment sharing; Notebook versioning
Statistics
Stacks 20	Stacks 16
Followers 62	Followers 38
Votes 0	Votes 2
Pros & Cons
No community feedback yet	Pros 1 Supports both gremlin and openCypher query languages 1 Aws managed services Cons 1 Doesn't have much community support 1 Doesn't have proper clients for different lanuages 1 Doesn't have much support for openCypher clients
Integrations
No integrations available	PyTorch Keras R Language MLflow Matplotlib

What are some alternatives to Amazon Personalize, Neptune?

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.