Data Observability for Machine Learning Teams
Kyle Kirwan, CEO & Co-founder, Bigeye
The ML data engineering conference presented by 
apply() Session + Video Archive
Machine Learning in Production: What I learned from monitoring 30+ models
Lina Weichbrodt, Machine Learning Lead Engineer, DKB AG
Wild Wild Tests: Monitoring Recommender Systems in the Wild
Jacopo Tagliabue, Director of AI, Coveo
Federico Bianchi, Postdoctoral Researcher, Bocconi University
ralf: Real-time, Accuracy Aware Feature Store Maintenance
Sarah Wooders, PhD Student, UC Berkeley - RISELab
More ethical machine learning using model cards at Wikimedia
Chris Albon, Director of Machine Learning, Wikimedia Foundation
Workshop: The Key Pillars of ML Observability and How to Apply Them to Your ML Systems
Amber Roberts, Machine Learning Engineer, Arize AI
Machine Learning Platform for Online Prediction and Continual Learning
Chip Huyen, Co-Founder & CEO, Claypot AI
Model Calibration in the Etsy Ads Marketplace
Erica Greene, Senior Engineering Manager, Etsy
Seoyoon Park, Senior Software Engineer, Etsy
Building Malleable ML Systems through Measurement, Monitoring & Maintenance
Karan Goel, PhD Student, Stanford University
ML Observability: Critical Piece of the ML Stack
Aparna Dhinakaran, Co-Founder & Chief Product Officer, Arize AI
Data Observability: The Next Frontier of Data Engineering
Barr Moses, CEO & Co-Founder, Monte Carlo
MLOps Done Right with Centralized Model Performance Management Powered by XAI
Krishna Gade, Co-Founder and CEO, Fiddler