Moody's Analytics Data Science with Domino

Moody's struggled to scale customized model delivery: development cycles took up to nine months, computing access was limited, and siloed R/SAS teams couldn't easily collaborate, driving up 'the cost of improving or replacing a model,' per GM Jacob Grotta. Moody's adopted Domino's platform on AWS, supporting RStudio, Jupyter, Python, Spark and SAS with automatic reproducibility tracking and centralized collaboration. End-to-end model development time fell from nine to four months, models reached production 6x faster, monitoring capacity quadrupled, and models were validated quarterly instead of annually; a Financial Statement Quality Checker deployed in days instead of a year, and the 40-model RiskCalc suite became rapidly customizable. Grotta said Domino 'accelerates our speed to delivery, providing a much faster and better return on our modeling investment.'

Domino Data Lab is an enterprise MLOps platform built for regulated industries — the sanctioned sandbox where biopharma teams run reproducible experiments under GxP/21 CFR Part 11 guardrails.

This sits in the Design & Stress-Test stage of the AI use-case lifecycle: Named examples from the tools pharma teams use to scope, prototype and benchmark an AI use case before committing to build it — data-catalog, sandbox and evaluation platforms.