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  5. Algorithms.io vs PredictionIO vs wise.io

Algorithms.io vs PredictionIO vs wise.io

OverviewComparisonAlternatives

Overview

wise.io
wise.io
Stacks2
Followers8
Votes0
Algorithms.io
Algorithms.io
Stacks48
Followers77
Votes0
PredictionIO
PredictionIO
Stacks67
Followers110
Votes8

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Detailed Comparison

wise.io
wise.io
Algorithms.io
Algorithms.io
PredictionIO
PredictionIO

Wise.io builds machine intelligence products that make it easy for companies to derive actionable insight from their greatest corporate resource: their data.

Build And Run Predictive Applications For Streaming Data From Applications, Devices, Machines and Wearables

PredictionIO is an open source machine learning server for software developers to create predictive features, such as personalization, recommendation and content discovery.

Use Wise.io for: Fraud detection, Intelligent sensors, Ad Targeting & Personalization, Genomics, Business Analytics, Finance, Healthcare, Sentiment Analysis;Dead simple machine learning.- Our intuitive, easy-to-use platform for machine learning enables anyone to build and deploy models with a few simple clicks.;A data science marketplace.- With the feature marketplace, we provide companies access to an expansive knowledge base.;State-of the art technology.- Our IP is 10-100x faster and more memory efficient than any other implementation we can find.;From experiment to production.- By breaking the barrier between sandbox learning and large-scale production environments, we decrease the lead time from inception to deployment.;Automated reports.- Every time you build a model, we generate an easy-to-read report detailing the insights gleaned from your data and the performance of your newly minted model.;Public or private cloud.- Our hosted platform makes it easy for businesses to deploy machine intelligence without having to build the infrastructure. For companies with security or latency concerns, we gladly offer an on-premise solution.
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
Integrated with state-of-the-art machine learning algorithms. Fine-tune, evaluate and implement them scientifically.;Customize the modularized open codebase to fulfill any unique prediction requirement.;Built on top of scalable frameworks such as Hadoop and Cascading. Ready to handle data of any scale.;Build powerful features in minutes, not months. Streamline the data engineering process.
Statistics
Stacks
2
Stacks
48
Stacks
67
Followers
8
Followers
77
Followers
110
Votes
0
Votes
0
Votes
8
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Pros
  • 8
    Predict Future

What are some alternatives to wise.io, Algorithms.io, PredictionIO?

TensorFlow

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

scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

PyTorch

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

Keras

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/

NanoNets

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

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

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

Polyaxon

An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.

Streamlit

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

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.

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