How to update machine learning models in production with streaming data?

To update ML models in production using streaming data:

  1. Use event-tracking tools like Snowplow to collect real-time user interactions.
  2. Stream this data into processing systems (e.g., Kafka, Spark, Flink) to derive fresh training data or features.
  3. Apply incremental learning or online learning techniques to update models continuously or in mini-batches.
  4. 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.

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Whether you’re modernizing your customer data infrastructure or building AI-powered applications, Snowplow helps eliminate engineering complexity so you can focus on delivering smarter customer experiences.