Podcast: Eduardo Toledo. Explore the step-by-step journey of creating a robust anti-money laundering detection system for casino transactions. Hosted by Eduardo Toledo, this episode demonstrates how dataset versioning with DVC, experiment tracking via MLflow, explainability using SHAP, and post-deployment monitoring with NannyML integrate to build a resilient and auditable machine learning pipeline.

Building a Production-Ready AML Detection Pipeline

Podcast: Eduardo Toledo.Discover how cutting-edge tools like DVC, MLflow, SHAP, and NannyML come together in Eduardo Toledo's pipeline to detect anti-money laundering in gambling and fintech industries. Hosted by Eduardo Toledo, this episode delves into the real challenges of deploying and maintaining adaptive machine learning models under fast-moving regulations and complex data. This series highlights the intersection of MLOps, explainable AI, and real-time monitoring in high-risk financial sectors.

Share Building a Production-Ready AML Detection Pipeline

Sign up to save your podcasts

Building a Production-Ready AML Detection Pipeline

Building a Production-Ready AML Detection Pipeline