About this Course
Enable Vectorization in Weaviate is a focused, intermediate course for developers and ML engineers ready to automate a critical part of the AI workflow. If you're tired of manually generating embeddings, this one-hour, hands-on course shows you how to make Weaviate do the heavy lifting for you. You will learn to enable and configure Weaviate's built-in vectorizer modules, such as those for OpenAI and Cohere, directly within your Docker environment. This course requires basic Docker and CLI skills, familiarity with APIs and vector embeddings, and Docker Desktop installed. This is a practical, job-oriented course. Through a guided project, you will configure a Weaviate instance, define a schema to trigger automatic vectorization, and ingest data to see it in action. Crucially, you will also learn to perform a cost-benefit analysis of this approach, equipping you to make and justify architectural decisions. By the end, you'll have the skill to deploy a more efficient, production-ready vector database
What You'll Learn
- Enable and configure Weaviate's vectorization modules
- Perform cost and performance analysis of embedding services
- Set up schema to trigger automatic vectorization
- Ingest data and monitor vectorization process
- Make architecture decisions based on cost-benefit analysis
Prerequisites
- Basic familiarity with Docker and CLI
- Understanding of APIs and vector embeddings
- Readiness for hands-on exercises and case studies
Instructors
LearningMate