Hands-on Monitoring

Effective ML Monitoring: A Hands-on Example

NannyML’s ML monitoring workflow is an easy, repeatable and effective way to ensure your models keep performing well in production.

Don’t Drift Away with Your Data: Monitoring Data Drift from Setup to Cloud

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

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

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

How to monitor ML models with NannyML SageMaker Algorithms

A walkthrough on how to deploy NannyML Monitoring Algorithms via AWS Marketplace and SageMaker

A deep dive into nannyML quickstart

Tutorial: Monitoring an ML Model with NannyML and Google Colab

How to Deploy NannyML in Production: A Step-by-Step Tutorial

Let’s dive into the process of setting up a monitoring system using NannyML with Grafana, PostgreSQL, and Docker.

AI is building paperclips, here is our secret plan on how to stop it