The Post Deployment Data Science Blog

Don’t let yourself be fooled by data drift

91% of ML Models degrade in time

A closer look to a paper from MIT, Harvard and other institutions showing how ML model’s performance tend to degrade in time.

Understanding Data Shift: Impact on Machine Learning Model Performance

Detecting Concept Drift: Impact on Machine Learning Performance

When should I retrain my model?

Monitoring Strategies for Demand Forecasting Machine Learning Models

Demand forecasting cases are one of the most challenging models to monitor post-deployment.

Monitoring Workflow for Machine Learning Systems

Understanding Data Shift: Impact on Machine Learning Model Performance

Detecting Covariate Shift: A Guide to the Multivariate Approach

Good old PCA can alert you when the distribution of your production data changes.

Data Drift Detection for Continuous Variables: Exploring Kolmogorov-Smirnov Test

Automating post-deployment Data Collection for ML Monitoring

How to Estimate Performance and Detect Drifting Images for a Computer Vision Model?

Are your NLP models deteriorating post-deployment? Let’s use unlabeled data to find out