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Unlock Multimodal Search

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Unlock Multimodal Search

This course is part of Vector DB Foundations, Embeddings & Search Algorithms Specialization

Instructor: LearningMate

Included with

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2 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

1 hour to complete

Flexible schedule

Learn at your own pace

2 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

1 hour to complete

Flexible schedule

Learn at your own pace

What you'll learn

Configure Weaviate to store and query linked image and text embeddings and analyze the precision gains of multimodal search.

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

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This course is part of the Vector DB Foundations, Embeddings & Search Algorithms Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 2 modules in 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.

Module details

This module provides the foundation for multimodal search. You will learn how to configure a Weaviate instance and design a schema that can store both image and text embeddings for a single object, preparing your database for cross-modal queries.

What's included

1 video1 reading2 assignments

Now that your database is configured, this module focuses on execution and validation. You will learn how to perform cross-modal queries to find text from an image and, critically, how to analyze the accuracy of your results using precision metrics.