Introduction to AI-Native Vector Databases
Offered By: LinkedIn Learning
Course Description
Overview
Learn how data and AI professionals can optimize data systems using AI.
Syllabus
Introduction
- Learning AI-native vector databases
- What you should know
- The superpower of vector databases
- Structured versus unstructured data
- Human-understandable versus machine-understandable data
- Drawing out and visualizing vector representations of data
- Introduce the concept of distance between two vectors
- Challenge: Working with vectors
- Solution: Working with vectors
- Frame the query as a question or search
- Generate the question in machine-understandable language
- Adding data to a vector database
- Performing semantic searches using Weaviate
- Challenge: Vector search with Weaviate
- Solution: Vector Search with Weaviate
- Machine learning models and object classification
- Translating data from human to machine-understandable
- ML models and vector embeddings
- Challenge: Search with images and text
- Solution: Search with images and text
- Scalability: When to use a vector DB
- Ways to measure performance of a vector DB
- CRUD operations in vector DBs
- Challenge: CRUD and performance
- Solution: CRUD and performance
- Vector DB1: E-commerce RecSys
- Vector DB2: Hybrid search
- Vector DB3: Retrieval augmented generation
- Challenge: Vector DBs
- Solution: Vector DBs
- Continue your AI-native vector databases learning journey
Taught by
Zain Hasan
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