  Explainability techniques for data-driven predictive models based on
artificial intelligence and machine learning algorithms allow us to better
understand the operation of such systems and help to hold them accountable. New
transparency approaches are developed at breakneck speed, enabling us to peek
inside these black boxes and interpret their decisions. Many of these
techniques are introduced as monolithic tools, giving the impression of
one-size-fits-all and end-to-end algorithms with limited customisability.
Nevertheless, such approaches are often composed of multiple interchangeable
modules that need to be tuned to the problem at hand to produce meaningful
explanations. This paper introduces a collection of hands-on training materials
-- slides, video recordings and Jupyter Notebooks -- that provide guidance
through the process of building and evaluating bespoke modular surrogate
explainers for tabular data. These resources cover the three core building
blocks of this technique: interpretable representation composition, data
sampling and explanation generation.
