To update ML models in production using streaming data:
- Use event-tracking tools like Snowplow to collect real-time user interactions.
- Stream this data into processing systems (e.g., Kafka, Spark, Flink) to derive fresh training data or features.
- Apply incremental learning or online learning techniques to update models continuously or in mini-batches.
- Redeploy updated models automatically or trigger retraining on a schedule using orchestration tools.
This enables models to stay current with changing user behavior or environmental conditions without retraining from scratch on the full dataset.