Machine learning and AI in financial services operate under constraints that most industries never face: every model decision must be explainable to regulators, every prediction must be auditable for years, and a single bad model can trigger fines measured in hundreds of millions. This guide covers the production patterns we have used to deploy ML and AI systems at banks, hedge funds, and fintechs. ...
As fine-tuned language models move from experimental to production in financial services, a new challenge emerges: how do you know when your model’s performance has degraded? A model that performed well at deployment may drift over time. Distribution of inputs changes. Market conditions shift. New financial instruments appear with vocabulary the model was not trained on. The risk is not just poor performance — it is confidently wrong predictions embedded in automated workflows processing millions of dollars in transactions. The Model Observatory pattern addresses this. It is a structured approach to detecting drift across three dimensions — data drift, model drift, and concept drift — with tooling and processes designed for regulated environments. ...