This document provides an overview of generalised low-rank models (GLRM) in 4 parts: 1. It describes GLRM as a method to compress large datasets with minimal loss of accuracy for memory reduction, faster machine learning, and feature engineering. 2. Four examples show how GLRM can be used for data compression, accelerating machine learning, visualizing clusters, and imputing missing values. 3. The technical references for GLRM are provided. 4. The presenter provides contact information and resources for learning more about GLRM and H2O.ai.