Deploy machine learning models in production
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What is

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. is a tool in the Machine Learning Tools category of a tech stack. is an open source tool with 1.4K GitHub stars and 69 GitHub forks. Here’s a link to's open source repository on GitHub Integrations

TensorFlow, scikit-learn, Keras, XGBoost, and PyTorch are some of the popular tools that integrate with Here's a list of all 5 tools that integrate with

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Here’s a list of reasons why companies and developers use
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  • Autoscaling
  • Supports TensorFlow, Keras, PyTorch, Scikit-learn, XGBoost, and more
  • CPU / GPU support
  • Rolling updates
  • Log streaming
  • Prediction monitoring
  • Minimal declarative configuration Alternatives & Comparisons

What are some alternatives to
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 is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano.
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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