How to Detect Model Drift in ML Monitoring

Aitechpark22 Jan, 2021Technology

Amit Paka, Founder and Chief Product Officer at FiddlerAI talks about the ways of detecting model drift in Machine Learning Monitoring � from identifying prediction drift in real-time models to drill down into the selected time window to view drift in underlying fratures. AI adoption is rapidly rising across industries. With the advent of Covid-19, digital adoption by consumers and businesses has vaulted five years forward in a matter of eight weeks. However, the complexity of deploying ML has hindered the success of AI systems. MLOps and specifically the productionizing of ML models come with challenges similar to those that plagued software prior to the arrival of DevOps Monitoring.