Building Multi-Modal Search with Vector Databases

This workshop is all about leveraging the power of lightning fast vector search implemented using vector databases like Weaviate in conjunction with multimodal embedding models to power at-scale, production ready applications capable of understanding and searching text, images, audio and video data.

What the attendees will take away from the workshop:

How machine learning models can embed multimodal data
How vector databases like Weaviate enable real time semantic search
How vector database can be used to scale the use of these models to billion scale
Code implementations of performing any-to-any modality search (for example audio-to-image or image-to-text searches)
Applications enabled by at scale multimodal search and retrieval
This event is inspired by DeepLearning.AI’s GenAI short courses, created in collaboration with AI companies across the globe. Our courses help you learn new skills, tools, and concepts efficiently within 1 hour.

About Weaviate

Weaviate is an open-source vector database. It allows you to store data objects and vector embeddings from your favorite ML-models, and scale seamlessly into billions of data objects.

Speakers:

Sebastian Witalec, Head of DevRel at Weaviate

  / sebawita  

Zain Hasan, Developer Advocate at Weaviate

  / zainhas  

Here are the slides covered in the presentation:

https://docs.google.com/presentation/...