More than a Cache: Turning Redis into a Composable, ML Data Platform // Samuel Partee // Coffee Sessions #111
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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 Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/) Connect with Mihail on LinkedIn: https://www.linkedin.com/in/mihaileric/) 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