More than a Cache: Turning Redis into a Composable, ML Data Platform

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.

Take-aways

Two main topics: - Feature stores - When you need offline and online feature store. - How to scale without scaling cost - Feature platforms - managed vs OSS and self hosted - When to use a feature platform - Redis is more than just a cache. Module system (RedisJSON, RedisSearch, RedisTimeSeries, etc) extend database capability. - JSON: document database - TS: time series database - Search: vector database - Vector embedding/search - Vector similarity search in public preview. Turns Redis into a vector database. - Demo link and repository are listed for sample full-stack application.

In this episode

Sam Partee

Principal Applied AI Engineer, Redis

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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Demetrios Brinkmann

Host

Demetrios is one of the main organizers of the MLOps community and currently resides in a small town outside Frankfurt, Germany. He is an avid traveller who taught English as a second language to see the world and learn about new cultures. Demetrios fell into the Machine Learning Operations world, and since, has interviewed the leading names around MLOps, Data Science, and ML. Since diving into the nitty-gritty of Machine Learning Operations he felt a strong calling to explore the ethical issues surrounding ML. When he is not conducting interviews you can find him making stone stacking with his daughter in the woods or playing the ukulele by the campfire.

Mihail Eric

Host

Mihail optimizes for impact. he strives to work with talented people to do amazing things. He's an engineer, researcher, and educator that has helped start teams at innovative organizations such as Amazon Alexa and RideOS. Mihail runs Pametan, a consultancy helping companies across verticals deliver machine learning and data-driven solutions to their hardest problems with a special focus on NLP, recommendation systems, tabular data, and computer vision domains. They’ve helped teams deliver 33% lift on key business metrics in <6 weeks. Mihail also built Confetti AI, the premier educational platform for training the next generation of machine learning practitioners.