On Juggling, Dr. Seuss and Feature Stores for Real-time AI/ML
Speakers

Nava is a Developer Advocate for Data Science and MLOps at Redis. She started her career in tech with an R&D Unit in the IDF and later had the good fortune to work with and champion Cloud, Big Data, and DL/ML/AI technologies just as the wave of each of these was starting.
Nava is also a mentor at the MassChallenge accelerator and the founder of LerGO—a cloud-based EdTech venture. In her free time, she enjoys cycling, 4-ball juggling, and reading fantasy and sci-fi books.

At the moment Demetrios is immersing himself in Machine Learning by interviewing experts from around the world in the weekly MLOps.community meetups. Demetrios is constantly learning and engaging in new activities to get uncomfortable and learn from his mistakes. He tries to bring creativity into every aspect of his life, whether that be analyzing the best paths forward, overcoming obstacles, or building lego houses with his daughter.
SUMMARY
Real-time ML-based applications are on the rise but deploying them at scale for large datasets with low latency and high throughput is challenging. This talk discusses the important role of feature stores for machine learning in deploying these applications.
By exploring a number of use cases in production, we see how the choice of online data store and the feature store data architecture play important roles in determining its performance and cost. Throughout the presentation, Nava illustrates key points by connecting them to juggling and Dr. Seuss! Stay tuned :)
// Related links
KDnuggets article: https://www.kdnuggets.com/2022/03/feature-stores-realtime-ai-machine-learning.html
Feast with Redis Quickstart tutorial: https://redis.com/blog/feast-with-redis-tutorial-for-machine-learning/
Linkedin: https://il.linkedin.com/in/nava1
LerGO: www.lergo.org.il (focused on Hebrew, Arabic)
LinkedIn's Feathr on Azure with Redis Slack: feathrAI.slack.com
Slides: https://drive.google.com/file/d/1CXNBhgvGi16KPEuk2Nl5NsyQpgcXl99e/view?usp=sharing
Linkedin's Feathr on Azure with Redis: https://github.com/linkedin/feathr/blob/main/docs/quickstart.md
Feast with Redis quickstart: https://redis.com/blog/feast-with-redis-tutorial-for-machine-learning/
Feast on Azure with Redis: https://github.com/Azure/feast-azure
Building feature stores with Redis: https://redis.com/blog/building-feature-stores-with-redis-introduction-to-feast-with-redis
Feature stores for real-time AI and machine learning: https://www.kdnuggets.com/2022/03/feature-stores-realtime-ai-machine-learning.html
Feast feature store: https://feast.dev/blog/feast-benchmarks/
Tecton feature store: https://www.tecton.ai/blog/announcing-support-for-redis/
DoorDash feature store: https://doordash.engineering/2020/11/19/building-a-gigascale-ml-feature-store-with-redis/
Redis on Flash: https://redis.com/blog/redis-on-flash-now-3-7x-faster/
Ekata: Scaling Ekata's Identity Graph with Redis on Flash https://youtu.be/jFFMaZK3hXQ
AT&T: https://youtu.be/AXQt_oW9JEc
Block CashApp: https://www.youtube.com/watch?v=wOqBj1hst3M&t=3392s
iFood: Building a Real-time Feature Store with iFood: https://youtu.be/PsrYdjnCEvI
Better: Using Feast in a Ranking System: https://youtu.be/Vvfit9Slb8U
Uber: https://youtu.be/3Edcx1etACY Wix: https://youtu.be/E8839ENL-WY

