Vector, Graph, and DynamoDB

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Noah Gift

1:11:36

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01 - Picking a database.mp4

03:28

02 - Intro to Amazon Neptune.mp4

02:48

03 - Key Rust CLI.mp4

01:32

04 - Rust CLI Graph lab.mp4

02:41

05 - What is SQLite Key features.mp4

01:50

06 - ETL with SQLite.mp4

02:12

07 - ETL with SQLite Demo.mp4

03:35

08 - What is DynamoDB.mp4

02:29

09 - What are vector databases.mp4

02:10

10 - Using CRUD with DynamoDB and the CLI.mp4

03:15

11 - Using CRUD with DynamoDB and Python.mp4

03:23

12 - Using CRUD with DynamoDB and Rust.mp4

04:18

13 - Learn AWS CloudShell Demo.mp4

04:02

14 - Learn AWS CodeCatalyst Demo.mp4

05:21

15 - Learn AWS CodeWhisperer Demo.mp4

04:09

16 - Create a table with the CLI.mp4

01:22

17 - Populate table batch.mp4

01:24

18 - Query a table with values.mp4

02:02

19 - Project walkthrough.mp4

02:24

20 - Semantic search.mp4

04:33

21 - Quickstart Qdrant.mp4

02:09

22 - Qdrant Rust client.mp4

03:04

23 - Vector database architecture.mp4

02:05

24 - Enhance semantic search.mp4

02:56

25 - Graph databases.mp4

02:24

Description

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.

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Database Programming

Noah Gift

Instructor's Courses

Author of Practical MLOps, Enterprise MLOps, Developing on AWS with C#, Pragmatic AI, and Python for DevOps. Certified on Multiple MLOps certifications including Google-Professional Machine Learning Engineer and AWS Certified Machine Learning - Specialty. Adjunct Professor at Duke MIDS & Northwestern Graduate Data Science & AI. Held business roles including CTO, general manager, consulting CTO, and cloud architect. Consults with start-ups and other companies on machine learning and cloud architecture. AWS ML Hero, Python Software Foundation Fellow, AWS Subject Matter on Machine Learning, AWS Certified Solutions Architect, AWS Certified Machine Learning Specialist, AWS Certified Big Data Specialist, Google Certified Professional Architect, and AWS Academy Accredited Instructor. Published books, videos, and courses on cloud machine learning, DevOps, Python, Data Science, Big Data and AI. ★ Specialties ★ ° Cloud-native Machine Learning and AI ° Directly teaching cutting edge skills to students that lead to jobs ° Creating world-class content in all forms ° Building Companies ° Shipping new Products ° Leading and growing engineering teams ° Production Machine Learning, Deep Learning, Big Data, and AI ° Serverless Data Engineering ° Advising Early Stage Startups/Consulting CTO services ° Distributed Systems and Scalability

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