Santiago Víquez

There's Data Drift, But Does It Matter?

We used data drift signals to estimate model performance — so you don't have to

Guide: How to evaluate if NannyML is the right monitoring tool for you

Can we detect LLM hallucinations? — A quick review of our experiments

Automating post-deployment Data Collection for ML Monitoring

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

Monitoring Strategies for Demand Forecasting Machine Learning Models

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

A deep dive into nannyML quickstart

Tutorial: Monitoring Missing and Unseen values with NannyML

Understanding the EU AI Act as a Data Scientist

Monitoring Workflow for Machine Learning Systems

Don’t let yourself be fooled by data drift

Tutorial: Monitoring an ML Model with NannyML and Google Colab

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.

6 ways to address data distribution shift