Vector Databases: from Embeddings to Applications
In Collaboration With
Free for a limited time
- Build efficient, practical applications including hybrid and multilingual searches, for diverse industries.
- Understand vector databases and use them to develop GenAI applications without needing to train or fine-tune an LLM yourself.
- Learn to discern when best to apply a vector database to your application.
What you’ll learn in this course
Vector databases play a pivotal role across various fields, such as natural language processing, image recognition, recommender systems and semantic search, and have gained more importance with the growing adoption of LLMs.
These databases are exceptionally valuable as they provide LLMs with access to real-time proprietary data, enabling the development of Retrieval Augmented Generation (RAG) applications.
At their core, vector databases rely on the use of embeddings to capture the meaning of data and gauge the similarity between different pairs of vectors and sift through extensive datasets, identifying the most similar vectors.
This course will help you gain the knowledge to make informed decisions about when to apply vector databases to your applications. You’ll explore:
- How to use vector databases and LLMs to gain deeper insights into your data.
- Build labs that show how to form embeddings and use several search techniques to find similar embeddings.
- Explore algorithms for fast searches through vast datasets and build applications ranging from RAG to multilingual search.
Who should join?
Anyone who’s interested in understanding and applying vector databases in their applications.
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