Powering the Next Wave of Intelligent Devices with Machine Learning – Part 3

In the second part of this series, we explored how the BigML Node-RED bindings work in more detail and introduced the key concepts of input-output matching and node reification which will allow you to create more complex flows. In this third and final part of this introductory series, we are going to review what we know about inputs and outputs in a more systematic way, to introduce debugging facilities, and present an advanced type of node that allows you to inject WhizzML code directly into your flows. Each BigML node has a varying number of inputs and outputs, which are embedded in the message payload that Node-RED propagates across nodes. For example, the ensemble node has one input called dataset and one output called ensemble. You can change the input and output port labels when you need to connect two nodes whose inputs and outputs do not match. Say for example that a node has an output port label generically named resource and that you want to use that output value in a downstream node that requires a dataset input.