AutoML

AutoML involves the partial or complete automation of the machine learning application. A well known example is Google Cloud AutoML service, which allows developers to train machine learning models without the need of prior technical knowledge. The service is based on a simple and intuitive drag and drop interface.

Why is autoML important ?

  • It increases productivity by automating repetitive tasks. This enables the data scientist to focus more on the problem rather than on the models themselves
  • Automating the ML pipeline also helps to avoid manual errors
  • It is a step towards democratizing machine learning by making it accessible to everybody
Explorium delivers the end-game of every data science process - from raw, disconnected data to game-changing insights, features, and predictive models. Better than any human can.
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