Algorithms.io vs Amazon Personalize

Overview

Algorithms.io

Stacks48

Followers77

Votes0

Amazon Personalize

Stacks20

Followers62

Votes0

Algorithms.io vs Amazon Personalize: What are the differences?

Algorithms.io: Machine learning as a service for streaming data from connected devices. Build And Run Predictive Applications For Streaming Data From Applications, Devices, Machines and Wearables; 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.

Algorithms.io and Amazon Personalize belong to "Machine Learning as a Service" category of the tech stack.

Some of the features offered by Algorithms.io are:

Classification & Anomaly Detection- With our machine learning algorithms and your time series data, we can get up to 99% prediction accuracy on the state of the sensor. Algorithms include neural network, random forest, support vector machine and others.
Streaming Data Infrastructure- We provide the infrastructure for your streaming data as a service including a highly scalable time-series database and analytics capabilities.
Analytics Across All Your Devices- Capture and aggregate data from all of your devices to perform analytics across the entire dataset.

On the other hand, Amazon Personalize provides the following key features:

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

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Detailed Comparison

Algorithms.io	Amazon Personalize
Build And Run Predictive Applications For Streaming Data From Applications, Devices, Machines and Wearables	Machine learning service that makes it easy for developers to add individualized recommendations to customers using their applications.
Classification & Anomaly Detection- With our machine learning algorithms and your time series data, we can get up to 99% prediction accuracy on the state of the sensor. Algorithms include neural network, random forest, support vector machine and others.;Streaming Data Infrastructure- We provide the infrastructure for your streaming data as a service including a highly scalable time-series database and analytics capabilities.;Analytics Across All Your Devices- Capture and aggregate data from all of your devices to perform analytics across the entire dataset.;Random Forest, SVM, Decision Tree, Node.js, Streaming Data	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
Stacks 48	Stacks 20
Followers 77	Followers 62
Votes 0	Votes 0

What are some alternatives to Algorithms.io, 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.

Related Comparisons

Some of the features offered by Algorithms.io are:

Classification & Anomaly Detection- With our machine learning algorithms and your time series data, we can get up to 99% prediction accuracy on the state of the sensor. Algorithms include neural network, random forest, support vector machine and others.
Streaming Data Infrastructure- We provide the infrastructure for your streaming data as a service including a highly scalable time-series database and analytics capabilities.
Analytics Across All Your Devices- Capture and aggregate data from all of your devices to perform analytics across the entire dataset.

On the other hand, Amazon Personalize provides the following key features:

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

Algorithms.io vs Amazon Personalize