Algorithms.io
Algorithms.io

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BigML
BigML

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Algorithms.io vs BigML: What are the differences?

Developers describe Algorithms.io as "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. On the other hand, BigML is detailed as "Machine Learning, made simple. Predictive analytics for big data and not-so-big data". BigML provides a hosted machine learning platform for advanced analytics. Through BigML's intuitive interface and/or its open API and bindings in several languages, analysts, data scientists and developers alike can quickly build fully actionable predictive models and clusters that can easily be incorporated into related applications and services.

Algorithms.io and BigML 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, BigML provides the following key features:

  • REST API
  • bindings in Pyton, Java, Ruby, node.js, C#, Clojure, PHP, and more
  • several algorithms, including categorical & regression decision trees, ensembles of trees (random decision forest), cluster analysis and more
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- No public GitHub repository available -

What is Algorithms.io?

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

What is BigML?

BigML provides a hosted machine learning platform for advanced analytics. Through BigML's intuitive interface and/or its open API and bindings in several languages, analysts, data scientists and developers alike can quickly build fully actionable predictive models and clusters that can easily be incorporated into related applications and services.
Why do developers choose Algorithms.io?
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            What are some alternatives to Algorithms.io and BigML?
            Amazon SageMaker
            A fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale.
            Azure Machine Learning
            Azure Machine Learning is a fully-managed cloud service that enables data scientists and developers to efficiently embed predictive analytics into their applications, helping organizations use massive data sets and bring all the benefits of the cloud to machine learning.
            Amazon Machine Learning
            This new AWS service helps you to use all of that data you’ve been collecting to improve the quality of your decisions. You can build and fine-tune predictive models using large amounts of data, and then use Amazon Machine Learning to make predictions (in batch mode or in real-time) at scale. You can benefit from machine learning even if you don’t have an advanced degree in statistics or the desire to setup, run, and maintain your own processing and storage infrastructure.
            Amazon Elastic Inference
            Amazon Elastic Inference allows you to attach low-cost GPU-powered acceleration to Amazon EC2 and Amazon SageMaker instances to reduce the cost of running deep learning inference by up to 75%. Amazon Elastic Inference supports TensorFlow, Apache MXNet, and ONNX models, with more frameworks coming soon.
            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.
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