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Fritz
Fritz

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GraphPipe
GraphPipe

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Fritz vs GraphPipe: What are the differences?

Fritz: Mobile machine learning made easy. Fritz is the end-to-end solution for on-device ML. Create ML-powered features in your app with ease and cross-platform support; GraphPipe: Machine Learning Model Deployment Made Simple, by Oracle. GraphPipe is a protocol and collection of software designed to simplify machine learning model deployment and decouple it from framework-specific model implementations.

Fritz and GraphPipe belong to "Machine Learning Tools" category of the tech stack.

GraphPipe is an open source tool with 643 GitHub stars and 91 GitHub forks. Here's a link to GraphPipe's open source repository on GitHub.

- No public GitHub repository available -

What is Fritz?

Fritz is the end-to-end solution for on-device ML. Create ML-powered features in your app with ease and cross-platform support.

What is GraphPipe?

GraphPipe is a protocol and collection of software designed to simplify machine learning model deployment and decouple it from framework-specific model implementations.
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              What are some alternatives to Fritz and GraphPipe?
              TensorFlow
              TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
              scikit-learn
              scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
              Keras
              Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
              PyTorch
              PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.
              ML Kit
              ML Kit brings Google’s machine learning expertise to mobile developers in a powerful and easy-to-use package.
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