Guided automation for machine learning

Implementing a web-based blueprint for semi-automated machine learning, using the open source Knime Analytics Platform

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We propose here a blueprint solution for the automation of the machine learning lifecycle.

The price to pay for automated machine learning (aka AutoML) is the loss of control to a black box kind of model. While such a price might be acceptable for circumscribed data science problems on well-defined domains, it might prove a limitation for more complex problems on a wider variety of domains. In these cases, a certain amount of interaction with the end users is actually desirable. This softer approach to machine learning automation—the approach we take at Knime—is obtained via guided automation, a special instance of guided analytics.

As easy as the final application might look to the end user, the system running in the background can be quite complex and therefore not easy to create completely from scratch. To help you with this process, we created a blueprint of an interactive application for the automatic creation and training of machine learning classification models.

The blueprint was developed with Knime Analytics Platform, and it is available on our public repository.

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