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ML Kit
ML Kit

117
152
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0
TransmogrifAI
TransmogrifAI

3
8
+ 1
0
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ML Kit vs TransmogrifAI: 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; TransmogrifAI: Automated machine learning for structured data (by Salesforce). TransmogrifAI (pronounced trăns-mŏgˈrə-fī) is an AutoML library for building modular, reusable, strongly typed machine learning workflows on Spark with minimal hand tuning.

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

TransmogrifAI is an open source tool with 1.57K GitHub stars and 271 GitHub forks. Here's a link to TransmogrifAI's open source repository on GitHub.

- 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 TransmogrifAI?

TransmogrifAI (pronounced trăns-mŏgˈrə-fī) is an AutoML library for building modular, reusable, strongly typed machine learning workflows on Spark with minimal hand tuning
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            What are some alternatives to ML Kit and TransmogrifAI?
            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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