Vector, Graph, and DynamoDB

سرفصل های دوره

In this course, MLOps expert Noah Gift explores vector and graph databases, powerful tools for managing and extracting insights from large, complex datasets. As data volumes continue to grow, scalability is crucial. We'll learn how vector and graph databases can efficiently store data while maintaining relationships, enabling more advanced analytics. Through real-world examples, you'll see how these databases unlock scalability for machine learning, fraud detection, social networks, and more.

This course was created by Noah Gift. We are pleased to host this training in our library.

01 - Picking a database

02 - Intro to Amazon Neptune

03 - Key Rust CLI

04 - Rust CLI Graph lab

05 - What is SQLite Key features

06 - ETL with SQLite

07 - ETL with SQLite Demo

08 - What is DynamoDB

09 - What are vector databases

10 - Using CRUD with DynamoDB and the CLI

11 - Using CRUD with DynamoDB and Python

12 - Using CRUD with DynamoDB and Rust

13 - Learn AWS CloudShell Demo

14 - Learn AWS CodeCatalyst Demo

15 - Learn AWS CodeWhisperer Demo

16 - Create a table with the CLI

17 - Populate table batch

18 - Query a table with values

19 - Project walkthrough

20 - Semantic search

21 - Quickstart Qdrant

22 - Qdrant Rust client

23 - Vector database architecture

24 - Enhance semantic search

25 - Graph databases

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تولید کننده: LinkedIn Learning (Lynda)