apply(risk) 2023 | Fraud Prevention Best Practices In ML Observability & Emerging Approaches for Multivariate Drift Detection

by:
Dat Ngo, Data Scientist & ML Engineer, Arize

Fraud takes many forms across industries and is constantly evolving. Data scientists and MLOps professionals must similarly evolve in real time, staying a step ahead of model performance degradation and new attack vectors. In this 10-minute talk, we will cover best practices in ML observability for detecting and preventing fraud across industries. We will also discuss a novel approach to anomaly and drift detection using embeddings, UMAP dimensionality reduction, non-parametric clustering, and data visualization.

apply(): The ML data engineering Conference
Presented by Tecton

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