• Model Training Pipelines: Focus on collecting and processing historical data. This includes cleaning, transformation, aggregation, and feature engineering to build datasets for training ML models.
• Real-Time Inference Pipelines: Focus on delivering fresh, low-latency data to deployed models for live predictions. These pipelines often rely on streaming technologies (e.g., Kafka, Flink) to push Snowplow event data to models in real time.

Snowplow can support both use cases by supplying high-quality behavioral data to different parts of your ML pipeline infrastructure.