Introduction to AI-Native Vector Databases

سرفصل های دوره

The primary purpose of vector databases is to provide fast and accurate similarity search or nearest neighbor search capabilities. The integration of AI techniques in vector databases enhances their capabilities, improves search accuracy, optimizes performance, and enables more intelligent and efficient management of high-dimensional data. In this course, Zain Hasan introduces this foundational technology—which is already being used in industries like ecommerce, social media, and more. Zain covers everything from foundational concepts around AI-first vector databases to hands-on coding labs for question answering using LLMs.

01 - Introduction

01 - Learning AI-native vector databases

02 - What you should know

03 - The superpower of vector databases

02 - 1. Data What Data Do Vector Databases Store and How Is It Stored

01 - Structured versus unstructured data

02 - Human-understandable versus machine-understandable data

03 - Drawing out and visualizing vector representations of data

04 - Introduce the concept of distance between two vectors

05 - Challenge Working with vectors

06 - Solution Working with vectors

03 - 2. Natural Querying How Do You Search for Data in a Vector Database

01 - Frame the query as a question or search

02 - Generate the question in machine-understandable language

03 - Adding data to a vector database

04 - Performing semantic searches using Weaviate

05 - Challenge Vector search with Weaviate

06 - Solution Vector Search with Weaviate

04 - 3. Machine Learning Vectors How Does a Vector Database Understand Your Data

01 - Machine learning models and object classification

02 - Translating data from human to machine-understandable

03 - ML models and vector embeddings

04 - Challenge Search with images and text

05 - Solution Search with images and text

05 - 4. Scalability What Does a Vector Database Need to Do

01 - Scalability When to use a vector DB

02 - Ways to measure performance of a vector DB

03 - CRUD operations in vector DBs

04 - Challenge CRUD and performance

05 - Solution CRUD and performance

06 - 5. Demonstrate Vector DBs and Use Cases

01 - Vector DB1 E-commerce RecSys

02 - Vector DB2 Hybrid search

03 - Vector DB3 Retrieval augmented generation

04 - Challenge Vector DBs

05 - Solution Vector DBs

07 - Conclusion

01 - Continue your AI-native vector databases learning journey

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تولید کننده: LinkedIn Learning (Lynda)