An end-to-end MLOps pipeline typically includes the following stages:
- Data Collection: Capture real-time behavioral data using tools like Snowplow.
Feature Engineering: Enrich and transform data into features in your data platform (e.g., using dbt on Snowflake or Databricks). - Model Training: Train models on historical datasets prepared from enriched data.
- Deployment: Push models into production for serving real-time predictions.
- 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.
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