Case Study: ML Feature Engineering

Problem

Feature extraction for a recommendation system bottlenecked model training.

Solution

Compiled user interaction aggregation and normalisation functions.

Results

Feature generation sped up 3.2x, reducing training pipeline from 12 hours to 4 hours.