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ML Kit

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TensorFlow.js

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ML Kit vs TensorFlow.js: What are the differences?

ML Kit: 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; TensorFlow.js: Machine Learning in JavaScript. Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API.

ML Kit and TensorFlow.js belong to "Machine Learning Tools" category of the tech stack.

TensorFlow.js is an open source tool with 11.1K GitHub stars and 801 GitHub forks. Here's a link to TensorFlow.js's open source repository on GitHub.

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Pros of ML Kit
Pros of TensorFlow.js
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    • 4
      NodeJS Powered
    • 4
      Open Source
    • 1
      Deploy python ML model directly into javascript

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    - No public GitHub repository available -

    What is ML Kit?

    ML Kit brings Google’s machine learning expertise to mobile developers in a powerful and easy-to-use package.

    What is TensorFlow.js?

    Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API

    Need advice about which tool to choose?Ask the StackShare community!

    What companies use ML Kit?
    What companies use TensorFlow.js?
    See which teams inside your own company are using ML Kit or TensorFlow.js.
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    What tools integrate with ML Kit?
    What tools integrate with TensorFlow.js?
      No integrations found
      What are some alternatives to ML Kit and TensorFlow.js?
      Tensorflow Lite
      It is a set of tools to help developers run TensorFlow models on mobile, embedded, and IoT devices. It enables on-device machine learning inference with low latency and a small binary size.
      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.
      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.
      Keras
      Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
      scikit-learn
      scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
      See all alternatives