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LinkedIn Learning

Introduction to AI-Native Vector Databases

via LinkedIn Learning

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
1. Data: What Data Do Vector Databases Store and How Is It Stored?
  • 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
2. Natural Querying: How Do You Search for Data in a Vector Database?
  • 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
3. Machine Learning Vectors: How Does a Vector Database Understand Your Data?
  • 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
4. Scalability: What Does a Vector Database Need to Do?
  • 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
5. Demonstrate Vector DBs and Use Cases
  • Vector DB1: E-commerce RecSys
  • Vector DB2: Hybrid search
  • Vector DB3: Retrieval augmented generation
  • Challenge: Vector DBs
  • Solution: Vector DBs
Conclusion
  • Continue your AI-native vector databases learning journey

Taught by

Zain Hasan

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4.8 rating at LinkedIn Learning based on 91 ratings

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