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Firebase Predictions
Firebase Predictions

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GraphLab Create
GraphLab Create

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Firebase Predictions vs GraphLab Create: What are the differences?

Developers describe Firebase Predictions as "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. On the other hand, GraphLab Create is detailed as "Machine learning platform that enables data scientists and app developers to easily create intelligent apps at scale". Building an intelligent, predictive application involves iterating over multiple steps: cleaning the data, developing features, training a model, and creating and maintaining a predictive service. GraphLab Create does all of this in one platform. It is easy to use, fast, and powerful.

Firebase Predictions and GraphLab Create can be primarily classified as "Machine Learning as a Service" tools.

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, GraphLab Create provides the following key features:

  • Analyze terabyte scale data at interactive speeds, on your desktop.
  • A Single platform for tabular data, graphs, text, and images.
  • State of the art machine learning algorithms including deep learning, boosted trees, and factorization machines.
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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 GraphLab Create?

Building an intelligent, predictive application involves iterating over multiple steps: cleaning the data, developing features, training a model, and creating and maintaining a predictive service. GraphLab Create does all of this in one platform. It is easy to use, fast, and powerful.
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              What are some alternatives to Firebase Predictions and GraphLab Create?
              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.
              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.
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