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ml pipelines preparation

focus on feature engineering, feature stores, offline/online sync, model training and serving, and drift detection.

key concepts

  • feature engineering and feature store (feast)
  • offline/online sync for ml pipelines
  • retraining workflow
  • drift detection and monitoring
  • data validation for ml features

explanation practice

  • feature store flow diagram
  • retraining dag diagram
  • drift detection illustration

projects

1. feature pipeline

  • extract from postgres → clean → feast store

2. retraining dag in airflow

  • automated scheduled model retrain

3. feature drift detection tool

  • compare distributions and flag anomalies