PRODUCTION SCENARIO
A retailer trained a churn model on a days-since-last-purchase feature that a nightly Apache Spark job computes. The web team reimplemented the same feature in application code for inference. Production predictions now differ from the offline results.
Which solution will address the root cause of this difference?
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Show answer and explanation
Answer: Compute the feature once and serve it from Amazon SageMaker Feature Store.
Two separate implementations of the same feature produce training-serving skew. Feature Store lets one feature processor ingest values that are read from the offline store for training and from the online store for low-latency inference, so both paths see identical features. Retraining, a larger model, and drift alerts leave the second implementation in place.
Official Source: https://docs.aws.amazon.com/sagemaker/latest/dg/feature-store.html ↗