Learn how to revolutionize search using Vector Similarity Search algorithms, which represent data as vectors to find similarities quickly. This course covers the basics of embeddings, vector databases, and indices, along with practical applications like recommendation systems and generative search methodology. The intended audience includes developers, data scientists, and AI enthusiasts looking to incorporate deep learning insights into their applications at scale. The teaching method involves a panel discussion with industry experts sharing their experiences and insights in the field.
Overview
Syllabus
– Introduction
– What are Embeddings.
– How to get embeddings?
– What are vector databases
– Types of indices, when you use them, and how to get access?
– How to use indices, and how to combine them with other services.
– Why is there an increased interest in this space?
– Day-to-day things used in workflows
– Contact Center Analytics using Speech API & Open AI
– Generative search methodology
– Recommendation systems and how vector search use case
– Off-the-shelves models for particular use cases
– One thing you’re excited about in this space
Taught by
Data Science Dojo
