Building Real-Time ML Features with Feast, Spark, Redis, and Kafka
Workshop: Building Real-Time ML Features with Feast, Spark, Redis, and Kafka
This workshop will focus on the core concepts underlying Feast, the open source feature store. We’ll explain how Feast integrates with underlying data infrastructure including Spark, Redis, and Kafka, to provide an interface between models and data. We’ll provide coding examples to showcase how Feast can be used to:
- Curate features in online and offline storage
- Process features in real-time
- Ensure data consistency between training and serving environments
- Serve feature data online for real-time inference
- Quickly create training datasets
- Share and re-use features across models
