This talk explores the transformative role of graph-based approaches in providing the necessary context to AI and ML systems. Graphs, with their inherent ability to represent relationships and interconnected data, offer a powerful framework for contextualization by creating more powerful data features, providing human-understandable explainability, and incorporating knowledge bases into data sets. 

We will delve into how graphs can enhance data representation, improve model accuracy, and enable more nuanced decision-making. Key topics include: Knowledge Graph fundamentals, how graphs support AI and ML models, public and private case studies, and emerging trends. By the end of this talk attendees will understand how graphs can be leveraged to provide the much-needed context in AI and ML algorithms, paving the way for more intelligent, context-aware systems.