The Post Deployment Data Science Blog

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

Harnessing the Power of AWS SageMaker & NannyML PART 1: Training and Deploying an XGBoost Model

A walkthrough on how to train, deploy and continuously monitor ML models using NannyML and AWS SageMaker.

Monitoring a Hotel Booking Cancellation Model Part 1: Creating Reference and Analysis Set

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

Estimating Model Performance without Ground Truth

It’s possible, as long as you keep your probabilities calibrated

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

Don’t let yourself be fooled by data drift

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