Introduction to Drifter ML: A Testing Framework for Machine Learning Models

Explore the world of machine learning model testing with this insightful conference talk from MLOps World: Machine Learning in Production. Discover Drifter-ML, a powerful testing framework designed to validate the assumptions made during model training. Join Eric Schles, a principal data scientist at Johns Hopkins University and PhD student at CUNY Graduate Center, as he delves into the motivations behind model testing and provides practical examples of implementing the Drifter-ML framework. Gain valuable knowledge on ensuring the reliability and consistency of your machine learning models in production environments. Learn how to leverage this innovative tool to enhance the robustness of your ML pipelines and improve overall model performance.

Introduction to Drifter ML