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Comet.ml
Comet.ml

5
16
+ 1
1
GraphPipe
GraphPipe

1
4
+ 1
0
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Comet.ml vs GraphPipe: What are the differences?

Developers describe Comet.ml as "Track, compare and collaborate on Machine Learning experiments". Comet.ml allows data science teams and individuals to automagically track their datasets, code changes, experimentation history and production models creating efficiency, transparency, and reproducibility. On the other hand, GraphPipe is detailed 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.

Comet.ml and GraphPipe can be categorized as "Machine Learning" tools.

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 Comet.ml?

Comet.ml allows data science teams and individuals to automagically track their datasets, code changes, experimentation history and production models creating efficiency, transparency, and reproducibility.

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 Comet.ml 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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