Meetup #101

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

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 :)


- Real-time AI/ML rely on super-fast data stores for serving features or data inputs for model online predictions/ inference - These data stores are often called ‘online stores’ or ‘online feature stores’ and are the critical component of the real-time data layer for AI/ML - There are significant differences in the performance and cost of feature stores, depending on the architecture, supported types of features, and components selected (as the feature server and online store). - Online Stores together with Offline Stores make up an emerging logical component called Feature Store, which is becoming the cornerstone of MLOps - Microsoft Azure SQL DB, Google BigQuery, AWS Redshift, and Snowflake are examples of excellent choices for offline store - There are different architectures and implementation options for implementing a Feature Store - from open-source, build from scratch, buy or subscribe  - Redis OSS is a great option to start with for the online store, moving on to Redis Enterprise Cloud as you scale for higher availability, persistence, management simplicity, 24x7 support, and cost savings with multi-tenancy and Redis on Flash

In this episode

Nava Levy

Nava Levy

Developer Advocate for Data Science and MLOps, Redis

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.


Demetrios Brinkmann

Demetrios Brinkmann


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.