An end-to-end MLOps pipeline typically includes the following stages:

1. Data Collection: Capture real-time behavioral data using tools like Snowplow.‍
2. Feature Engineering: Enrich and transform data into features in your data platform (e.g., using dbt on Snowflake or Databricks).
3. Model Training: Train models on historical datasets prepared from enriched data.
4. Deployment: Push models into production for serving real-time predictions.
5. Monitoring: Track model performance, detect drift, and trigger retraining when necessary.

Snowplow’s real-time data feeds can provide up-to-date inputs to support both model training and monitoring.