A Guide To Automated Model Retraining

While the industry has invested a lot in processes and techniques for knowing when to deploy a model into production, there is arguably less collective knowledge on the equally important task of knowing when to retrain a model. In truth, knowing when to retrain a model is hard due to factors like delays in feedback or labels for live predictions. In practice, many practitioners just end up training on a specific schedule — or not at all — and hope for the best. 

Based on direct experience working with customers with models in production topping billions of daily predictions, this guide covers:

  • How to shift to automated, dynamic model retraining
  • Best practices for metrics-driven retraining and strategies for promoting a new model and navigating retraining feedback loop data
  • How to calculate AI ROI


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