More than a Cache: Turning Redis into a Composable, ML Data Platform // Samuel Partee // Coffee Sessions #111

MLOps Coffee Sessions #111 with Samuel Partee, Principal Applied AI Engineer of Redis, More than a Cache: Turning Redis into a Composable, ML Data Platform co-hosted by Mihail Eric. This episode is sponsored by Redis.

// Abstract
Pushing forward the Redis platform to be more than just the web-serving cache that we've known it up to now. It seems like a natural progression for the platform, we see how they're evolving to be this AI-focused, AI native serving platform that does vector similarity, feature stored provides those kinds of functionalities.

// Bio
A Principal Applied AI Engineer at Redis, Sam helps guide the development and direction of Redis as an online feature store and vector database.   

Sam's background is in high-performance computing including ML-related topics such as distributed training, hyperparameter optimization, and scalable inference.

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// Related Links
https://partee.io
Redis VSS demo: https://github.com/Spartee/redis-vector-search
Redis Stack: https://redis.io/docs/stack/
Github - https://github.com/Spartee  
OSS org Sam co-founded at HPE/Cray - https://github.com/CrayLabs
This paper last year was some of the best research and collaborations Sam has been a part of. The Paper is published here: https://www.sciencedirect.com/science/article/pii/S1877750322001065?via%3Dihub
Do you really need an extra database for vectors? https://databricks.com/dataaisummit/session/emerging-data-architectures-approaches-real-time-ai-using-redis
Blink: The Power of Thinking Without Thinking by Malcolm Gladwell,  Barry Fox,  Irina Henegar (Translator): https://www.goodreads.com/book/show/40102.Blink

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Connect with Sam on LinkedIn: www.linkedin.com/in/sam-partee-b04a1710a

Timestamps:
[00:00] Introduction to Samuel Partee
[00:24] Takeaways
[02:46] Updates on the Community
[05:17] Start of Redis
[08:10] Vision for Vector Search
[11:05] Changing the narrative going from the "Cache" for all servers and web endpoints
[14:35] Clear value prop on demos
[20:17] Vector Database
[26:26] Features with benefits
[28:41] AWS Spend
[30:39] Vector Database upsell model and bureaucratic convenience  
[32:08] Distributed training hyperparameter optimization and scalable inference
[35:03] Core infrastructural advancement
[36:55] Tools movement to help
[39:00] Using Machine Learning at scale in numerical simulations with SmartSim: An application to ocean climate modeling (published paper) [42:52] Future applications of tech to get excited with
[44:20] Lightning round
[47:48] Wrap up