Understanding H2O AutoML Architecture and Training

Model training is one of the core components of a Machine Learning (ML) pipeline. It is the step in the pipeline where the system reads and understands the patterns in the dataset. This learning outputs a mathematical representation of the relationship between the different features in the dataset and the target value. The way in which the system reads and analyzes data depends on the ML algorithm being used and its intricacies. This is where the primary complexity of ML lies. Every ML algorithm has its own way of interpreting the data and deriving information from it. Every ML algorithm aims to optimize certain metrics while trading off certain biases and variances. Automation done by H2O AutoML further complicates this concept. Trying to understand how that would work can be overwhelming for many engineers.

Don’t be discouraged by this complexity. All sophisticated systems can be broken down into simple components. Understanding...