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Algorithms.io
Algorithms.io

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Azure Machine Learning
Azure Machine Learning

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Algorithms.io vs Azure Machine Learning: 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; Azure Machine Learning: A fully-managed cloud service for predictive analytics. 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.

Algorithms.io and Azure Machine Learning can be primarily classified as "Machine Learning as a Service" tools.

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, Azure Machine Learning provides the following key features:

  • Designed for new and experienced users
  • Proven algorithms from MS Research, Xbox and Bing
  • First class support for the open source language R
- No public GitHub repository available -
- 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 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.
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            What are some alternatives to Algorithms.io and Azure 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 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.
            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.
            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.
            FloydHub
            Platform-as-a-Service for training and deploying your DL models in the cloud. Start running your first project in < 30 sec! Floyd takes care of the grunt work so you can focus on the core of your problem.
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