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GraphPipe
GraphPipe

1
4
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MLflow
MLflow

11
21
+ 1
0
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GraphPipe vs MLflow: What are the differences?

What is 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.

What is MLflow? An open source machine learning platform. MLflow is an open source platform for managing the end-to-end machine learning lifecycle.

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

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, MLflow provides the following key features:

  • Track experiments to record and compare parameters and results
  • Package ML code in a reusable, reproducible form in order to share with other data scientists or transfer to production
  • Manage and deploy models from a variety of ML libraries to a variety of model serving and inference platforms

GraphPipe and MLflow are both open source tools. It seems that GraphPipe with 643 GitHub stars and 91 forks on GitHub has more adoption than MLflow with 23 GitHub stars and 13 GitHub forks.

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 MLflow?

MLflow is an open source platform for managing the end-to-end machine learning lifecycle.
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          What are some alternatives to GraphPipe and MLflow?
          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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