About this Course
Spin Up Weaviate is an intermediate, hands-on course for developers and ML engineers who need to get a modern vector database running fast. If you're ready to move from theory to practice, this course provides a direct, step-by-step path to deploying, configuring, populating, and querying Weaviate, one of the most popular open-source vector databases available today. Forget high-level concepts; this course is about execution. You will learn how to use Docker Compose to launch a Weaviate instance locally, define a data schema using its API, and ingest data objects for semantic search. Through a series of practical and guided screencasts and a final, real-world project, you will configure a live database, load it with a dataset of 1,000 articles, and perform your first vector search query using GraphQL APIs to run similarity-based vector search queries. By the end of this 2-hour session, you will have the confidence and skill to deploy and interact with a vector database environment for your own AI applications
What You'll Learn
- Deploy a Weaviate vector database with Docker Compose, configure a schema, ingest data objects, and run vector search queries using its native API
- Execute vector search queries efficiently using Weaviate’s native GraphQL interface
- Launch a local Weaviate instance and apply semantic search on data objects
- Load realistic datasets and perform similarity-based vector search
Prerequisites
- Basic familiarity with the topic and its common terminology
- Readiness to practice through applied exercises or case-based work
Instructors
LearningMate