Redis Crash Course for Artificial Intelligence & Machine Learning | Redis for Beginners
A Redis crash course for beginners and its applications for artificial intelligence & machine learning. As data scientists and machine learning professionals make the transition from theory to applied data science, they naturally expand their skill set beyond Python or Pandas. They need to understand how to leverage a key-value store or database to store their embeddings or features, and how to load and fetch them very quickly for online predictions or to perform complex operations in milliseconds for real-time use cases. Because of that, Redis — the super-fast in-memory database, is being increasingly used for machine learning: from caching, messaging, and fast data ingestion, to vector similarity search and online feature stores.
This crash course is intended for beginners with no prior experience with Redis. By the end of the session, you will know:
What a key-value store is, and the difference between Redis and SQL databases
Which key machine learning concepts and use cases Redis enables
Which data types and structures can be stored in Redis
Key database considerations for deploying real-time AI/ML at scale
Redis as an online feature store - why and how to get started
Redis as a vector database for embeddings and neural search - why and how to get started
Table of Content
00:00 – Introduction
4:26 – Setting up the context: hottest trends in AI/ML, defining real-time AI/ML, overview of Redis in AI/ML
8:46 – Key concepts: No SQL, key-value store, disk vs. in-memory databases, data types & structures
18:13 – Demo
21:26 – Operational aspect of deploying real-time AI/ML
25:32 – Redis as infra for online feature stores
36:06 – Intro to RediSearch: SQL queries for NoSQL
44:48 – Redis as vector database for neural search
53:09 – Summary
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