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GraphPipe
GraphPipe

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ML Kit
ML Kit

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GraphPipe vs ML Kit: What are the differences?

Developers describe GraphPipe as "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. On the other hand, ML Kit is detailed as "Machine learning for mobile developers (by Google)". ML Kit brings Google’s machine learning expertise to mobile developers in a powerful and easy-to-use package.

GraphPipe and ML Kit can be categorized as "Machine Learning" tools.

Some of the features offered by GraphPipe are:

  • A minimalist machine learning transport specification based on flatbuffers
  • Simple, efficient reference model servers for Tensorflow, Caffe2, and ONNX.
  • Efficient client implementations in Go, Python, and Java.

On the other hand, ML Kit provides the following key features:

  • Image labeling - Identify objects, locations, activities, animal species, products, and more
  • Text recognition (OCR) - Recognize and extract text from images
  • Face detection - Detect faces and facial landmarks

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 GraphPipe?

GraphPipe is a protocol and collection of software designed to simplify machine learning model deployment and decouple it from framework-specific model implementations.

What is 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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            What are some alternatives to GraphPipe and ML Kit?
            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.
            CUDA
            A parallel computing platform and application programming interface model,it enables developers to speed up compute-intensive applications by harnessing the power of GPUs for the parallelizable part of the computation.
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