Models that perform well offline often degrade in production due to data drift, latency constraints, and operational complexity. The difference between a prototype and a production system is the pipeline: data ingestion, feature computation, serving, and monitoring.
We standardize on feature stores and versioned datasets so training and serving use the same definitions. That eliminates "it worked in the notebook" failures. Automated retraining and A/B tests for model versions keep performance in check.
For fintech and e-commerce clients we focus on explainability, audit trails, and fallbacks. When the model is uncertain or inputs are out-of-distribution, we fall back to rules or human review—never silent failure.
Invest in observability from day one: feature drift, prediction distributions, and business metrics. The pipeline is the product; the model is one component.