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Firebase Predictions
Firebase Predictions

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NanoNets
NanoNets

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Firebase Predictions vs NanoNets: What are the differences?

What is Firebase Predictions? Define dynamic user groups based on predicted behavior. Firebase Predictions uses the power of Google’s machine learning to create dynamic user groups based on users’ predicted behavior.

What is NanoNets? Machine learning API with less data. 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.

Firebase Predictions and NanoNets belong to "Machine Learning as a Service" category of the tech stack.

Some of the features offered by Firebase Predictions are:

  • Boost revenue and retention through customized user experiences
  • Send smarter notifications
  • Create custom predictions

On the other hand, NanoNets provides the following key features:

  • Image categorization API with less than 30 images per category
  • Custom object localization API
  • Text deduplication API
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- No public GitHub repository available -

What is Firebase Predictions?

Firebase Predictions uses the power of Google’s machine learning to create dynamic user groups based on users’ predicted behavior.

What is 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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          What are some alternatives to Firebase Predictions and NanoNets?
          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.
          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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