Amarendra Kumar and Kulbhushan Pachauri presented on accelerating recommendations at scale using Redis. They discussed serving over 30 million consumers across 17 diverse markets with recommendations across over 300,000 movies and videos. Without Redis, their architecture struggled with the 36+ billion minutes of content consumed each month and meeting low recommendation latency SLAs. By caching content, consumer information, and configurations in Redis, they were able to improve performance and use it cost effectively to provide quick access for slower databases.

1. Amarendra Kumar
Principal Engineer
Kulbhushan Pachauri
Principal AI Architect
Accelerating Recommendations
at with Redis

2. PRESENTED
BY

3. PRESENTED
BY
1 Consumer Scale
Serving 30+ million consumers
2 Geographical Scale
Serving 17 vastly diverse markets
3 Content Scale
Recommendation across a growing content catalogue of 300,000+ movies, videos etc.
Challenges of Recommendations at Scale
4 Consumption Scale
Analyzing 36+ billion minutes of videos viewed
5 Recommendation Latency
Serving rapidly changing recommendations with low SLAs

4. PRESENTED
BY
Types of Recommendations : Top Trending
Read by many,
Recommended to user

5. PRESENTED
BY
Types of Recommendations : Because You Watched

6. PRESENTED
BY
Types of Recommendations : Top Picks for You

7. PRESENTED
BY
Behind the Scenes : Recommendation Architecture

8. PRESENTED
BY
Performance without Redis

9. PRESENTED
BY
Performance with Redis

10. PRESENTED
BY
Redis @ Viu
Content caching
Consumer Info caching
Double Billing Prevention
Consumer Configuration Caching
Cost effectiveness : Quick access for slower DBs
Google Cloud Memorystore
Redis on-prem

11. Thank You!