About this Course
"Unlock Multimodal Search" is an intermediate, hands-on course for developers and ML engineers ready to build the next generation of AI-powered search. Text-only search is no longer enough; this 90-minute course will teach you how to create applications that can search across different data types, such as finding text from an image. Using the powerful open-source vector database Weaviate, you will move from theory to a functioning demonstration. This course requires basic skills in Docker, APIs, Python, and the command line (CLI). Familiarity with vector databases. Docker Desktop must be installed. This course is focused on execution. You will learn to configure a Weaviate schema to handle both image and text embeddings for a single object, ingest multimodal data, and perform powerful cross-modal queries. Through a final, hands-on project that mirrors a real-world job task, you will not only build an image-to-text search demo but also learn how to measure its accuracy with precision metrics. By the end, you'll be equipped to architect and validate sophisticated, multimodal AI applications
What You'll Learn
- Configure Weaviate to store and query linked image and text embeddings
- Design a schema supporting multimodal data ingestion and queries
- Build a practical multimodal search demo and evaluate its accuracy
- Understand integration of cross-modal data for enhanced search precision
Prerequisites
- Basic familiarity with machine learning and vector databases
- Willingness to practice through exercises or case work
- Docker Desktop installed and command line usage skills
Instructors
LearningMate