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Cortex.dev
Cortex.dev

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Paperspace

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Paperspace vs Cortex.dev: What are the differences?

Developers describe Paperspace as "The way to access and manage limitless computing power in the cloud". It is a high-performance cloud computing and ML development platform for building, training and deploying machine learning models. Tens of thousands of individuals, startups and enterprises use it to iterate faster and collaborate on intelligent, real-time prediction engines. On the other hand, Cortex.dev is detailed as "Deploy machine learning models in production". It is an open source platform that takes machine learning models—trained with nearly any framework—and turns them into production web APIs in one command.

Paperspace and Cortex.dev can be primarily classified as "Machine Learning" tools.

Some of the features offered by Paperspace are:

  • Intelligent alert
  • Two-factor authentication
  • Share drives

On the other hand, Cortex.dev provides the following key features:

  • Autoscaling
  • Supports TensorFlow, Keras, PyTorch, Scikit-learn, XGBoost, and more
  • CPU / GPU support

Cortex.dev is an open source tool with 1.42K GitHub stars and 69 GitHub forks. Here's a link to Cortex.dev's open source repository on GitHub.

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What is Cortex.dev?

It is an open source platform that takes machine learning models—trained with nearly any framework—and turns them into production web APIs in one command.

What is Paperspace?

It is a high-performance cloud computing and ML development platform for building, training and deploying machine learning models. Tens of thousands of individuals, startups and enterprises use it to iterate faster and collaborate on intelligent, real-time prediction engines.
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              What are some alternatives to Cortex.dev and Paperspace?
              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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