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Neural Network Intelligence: Microsoft’s open source automated machine learning toolkit

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  • 2 min read
  • 01 Oct 2018

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Google’s Cloud AutoML now has competition; Microsoft have released an open-source automated machine learning toolkit of their own. Dubbed as Neural Network Intelligence, this toolkit will allow data scientists and machine learning developers to perform tasks such as neural architecture search and hyperparameter tuning with relative ease.

Per Microsoft’s official page, this toolkit will allow data scientists, machine learning developers and AI researchers with the necessary tools to customize their AutoML models across various training environments. The toolkit was announced in November 2017 and has been in the research phase for a considerable period of time, before it was released for public use recently.

Who can use the Neural Network Intelligence toolkit?


Microsoft’s highly anticipated toolkit for automated machine learning is perfect for you if:

  • You want to try out different AutoML algorithms for training your machine learning model
  • You want to run AutoML jobs in different training environments, including remote servers and cloud
  • You want to implement your own AutoML algorithms and compare their performance with other algorithms
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  • You want to incorporate your AutoML models in your own custom platform


With Neural Network Intelligence toolkit, data scientists and machine learning developers can train and customize their machine learning models more effectively. The tool is expected to go head to head with Auto-Keras, another open source AutoML library for deep learning. Auto-Keras has quickly generated quite a traction with more than 3000 stars on GitHub, suggested the growth in popularity of Automated Machine Learning.

You can download and learn more about this AutoML toolkit on their official GitHub page.

Read more


What is Automated Machine Learning (AutoML)?

Top AutoML libraries for building your ML pipelines

Anatomy of an automated machine learning algorithm (AutoML)