Machine learning as a service for streaming data from connected devices.
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What is

Build And Run Predictive Applications For Streaming Data From Applications, Devices, Machines and Wearables is a tool in the Machine Learning as a Service category of a tech stack.

Who uses


6 developers on StackShare have stated that they use

Why developers like

Here’s a list of reasons why companies and developers use
Top Reasons
Be the first to leave a pro's Features

  • 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 Alternatives & Comparisons

What are some alternatives to
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 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.
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.
See all alternatives's Followers
21 developers follow to keep up with related blogs and decisions.
Ritika Jain
Sanjeev Singh
Sajjad vafaie
John Alton
Ryan Hicks
bankon meos
Arun Kumar