Meetup #101

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

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 will discuss the important role of feature stores for machine learning in deploying these applications. By exploring a number of use cases in production, we will 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, we will illustrate key points by connecting them to juggling and Dr. Seuss! Stay tuned :)

Take-aways

- 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