16 Production infrastructure

This chapter covers

  • Implementing passive retraining with the use of a model registry
  • Utilizing a feature store for model training and inference
  • Selecting an appropriate serving architecture for ML solutions

Utilizing ML in a real-world use case to solve a complex problem is challenging. The sheer number of skills needed to take a company’s data (frequently messy, partially complete, and rife with quality issues), select an appropriate algorithm, tune a pipeline, and validate that the prediction output of a model (or an ensemble of models) solves the problem to the satisfaction of the business is daunting. The complexity of an ML-backed project does not end with the creation of an acceptably performing model, though. ...

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