nannyml

Testing out NannyML

This show cases the ML Engineering concepts geared towards taking a model to production , serving with batch inference and implementation of CI\CD

Collibra governance tooling — edge stack deployment, contract ID generation, data contract lifecycle, ORM/SBVR semantic pipeline, and API server. All features are exposed through the singine collibra CLI family.

This repository describes how to monitor a model using NannyML

Production-oriented MLOps workflow for NYC Green Taxi tip prediction using Metaflow, MLflow, data-quality gates, drift monitoring, automated retraining, and governed model promotion.

ML model monitoring for fraud detection using NannyML

Monitoreo de un modelo de fraude financiero con Python y NannyML: CBPE, deriva de datos y diagnóstico. Proyecto de portafolio basado en DataCamp.

On-call AI agent that detects ML model drift, walks DataHub lineage to the upstream root cause, identifies the owner, and writes a drift-causation object back onto the model.

Help the bank monitoring their fraud detection model and figuring out why it's not performing as expected.