Authors: Paolo Tamagnini, Simon Schmid, and Christian Dietz
Implementing a Web-based Blueprint for Semi-automated Machine Learning, using KNIME Analytics Platform
This article is a follow-up to our introductory article on the topic, “How to automate machine learning.” In this second post, we describe in more detail the techniques and algorithms happening behind the scenes during the execution of the web browser application, proposing 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.