MathWorks Modelscape Monitor vs NannyML

MathWorks Modelscape Monitor gives financial-services professionals a centralized view of financial model use, performance and status across the model lifecycle. It can display metrics such as data drift and fairness, while users can define custom metrics and visualizations. Teams can build dashboards and deploy them automatically. When monitoring status changes, Modelscape can email model owners, and evaluations include diagnostic views. The documented drift example uses MATLAB’s detectdrift function. Modelscape supports models built in MATLAB, Python, R, SAS and other programming languages. Its API provides HTTP endpoints usable from JavaScript, Java, Python, C++ and other HTTP-supported languages. Organizations contact MathWorks Consulting Services for hosting, then can install the MATLAB support package through Add-On Explorer. Governance logs every user action for audit compliance. MathWorks positions it for regulation-compliant model risk management and names SS 1/23, SR 11-7, OSFI E-23 and ECB TRIM.

NannyML monitors deployed machine-learning models and estimates their performance when ground truth is delayed or unavailable. It detects concept drift as well as multivariate and univariate data drift, and connects alerts to changes in model performance. Data-quality checks and ranked drift alerts can help teams investigate features associated with performance issues. Users can relate model performance to monetary or other business outcomes. NannyML Cloud supports webhooks for retraining actions and an SDK for automating monitoring-data ingestion. Cloud is available through Azure and AWS marketplaces as SaaS or a managed application. With the managed-application option, the application and required infrastructure are provisioned within the customer's cloud subscription. For its Azure managed application, NannyML documents encrypted traffic and disks, but says automated backups were still being implemented and advises customers to safeguard model inputs and outputs elsewhere. The open-source library installs with pip or conda. Direct local dataset uploads through the Cloud UI must be smaller than 100 MB. Plans include a free self-managed Open Source option, Starter at $399 a month, and Scale at $999 a month.