How Good Models Go Bad in Production

Training an accurate model is one thing, but ensuring it stays accurate in production is a whole other challenge. 91% of ML models degrade over time once they're deployed, with suboptimal feature engineering practices often being the culprit. This degradation occurs in two stages: pre-production and post-production. In pre-production, issues such as misinterpreted feature logic, inaccurate historical data, and incomplete training datasets can lead to poor model performance. Post-production, models face "model drift" due to changes in the world, including concept drift and data drift, where the relationship between inputs and outputs changes over time. To improve accuracy and prevent model demise, it's essential to get data scientists and engineers on the same page, combine different feature sources, generate accurate training datasets quickly, and rapidly iterate on features based on real-world feedback.