To build a machine learning pipeline with Snowplow and Databricks:

1. Collect event data using Snowplow trackers (web, mobile, and server-side)
2. Stream real-time event data into Databricks using Kafka or Kinesis
3. Use Apache Spark to clean, transform, and engineer features from the event data
4. Store processed data in Delta Lake for further analysis
5. Train machine learning models using Databricks' MLflow and monitor model performance in real time

This end-to-end pipeline allows for continuous updates to machine learning models based on real-time customer behavior.