Continuous Machine Learning vs Propel

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Continuous Machine Learning

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Propel

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Propel vs Continuous Machine Learning: What are the differences?

What is Propel? Machine learning for JavaScript. Propel provides a GPU-backed numpy-like infrastructure for scientific computing in JavaScript.

What is Continuous Machine Learning? CI/CD for Machine Learning Projects. Continuous Machine Learning (CML) is an open-source library for implementing continuous integration & delivery (CI/CD) in machine learning projects. Use it to automate parts of your development workflow, including model training and evaluation, comparing ML experiments across your project history, and monitoring changing datasets.

Propel and Continuous Machine Learning belong to "Machine Learning Tools" category of the tech stack.

Some of the features offered by Propel are:

  • Run anywhere, in the browser or natively from Node
  • Target multiple GPUs and make TCP connections
  • PhD optional

On the other hand, Continuous Machine Learning provides the following key features:

  • GitFlow for data science
  • Auto reports for ML experiments
  • No additional services

Propel is an open source tool with 2.78K GitHub stars and 79 GitHub forks. Here's a link to Propel's open source repository on GitHub.

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What is Continuous Machine Learning?

Continuous Machine Learning (CML) is an open-source library for implementing continuous integration & delivery (CI/CD) in machine learning projects. Use it to automate parts of your development workflow, including model training and evaluation, comparing ML experiments across your project history, and monitoring changing datasets.

What is Propel?

Propel provides a GPU-backed numpy-like infrastructure for scientific computing in JavaScript.

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What are some alternatives to Continuous Machine Learning and Propel?
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.
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.
See all alternatives