Model Data in Weaviate

About this course

Model Data in Weaviate is an intermediate, project-based course for developers and data professionals who want to unlock the full potential of vector search. In modern AI applications, search speed and relevance are everything, and they both begin with a powerful data model. This course teaches you the principles of designing a sophisticated, high-performance schema, moving beyond flat data structures to build a connected graph of information. You will learn to model complex relationships using multi-class schemas and relational links. Through a hands-on project using Weaviate as our implementation tool, you will design a schema for a real-world dataset, import interconnected data objects, and, most importantly, learn how to benchmark your design choices. The course culminates in an evaluation where you will use query latency data to prove the performance gains of your schema. You’ll leave with not just a working project, but also a repeatable methodology for optimizing data architecture in any advanced search application.

Course objectives

• develop a repeatable methodology for optimizing data architecture
• understand the principles of vector search and data modeling
• apply learned concepts in a practical, real-world project