Graph embeddings can be used for a variety of applications, including recommendation, fraud detection, and other machine learning tasks. In this work, we aim to walk through various different embedding techniques, starting with spectral approaches, moving towards graph neural networks, and finally newer, inductive techniques such as NodePiece. Throughout the tutorial, we will be implementing algorithms using the (open-source) TigerGraph Machine Learning Workbench, a tool for easily training machine learning algorithms on large-scale graph datasets. Using a variety of datasets, we will discuss the pros and cons of each technique, demonstrate them working, and examine future directions for the field of graph embedding research from the lens of industry.