This repo contains example notebooks and accompanying documentation for using Fiddler.
These example notebooks aim to give you a quick start on various Fiddler capabilities using different model tasks, data types, and use cases. They can also serve as a reference guide for setting up the monitoring of your own models in Fiddler.
Use the projects in this repo to onboard models and data to illustrate ML model and LLM application monitoring, analysis, and protection. This repo contains the example notebooks listed below. You can launch them in a Google Colab environment using the Colab links.
Note: This repository uses Git Large File Storage (Git LFS) for managing large files. Please make sure you have Git LFS installed before cloning this repository. You can find installation instructions at git-lfs.github.com. You can find the file types tracked via GitLFS at the
.gitattributesfile (currently only.csvfiles)
- LLM - Comparison
- LLM - Simple Monitoring Quickstart
- ML - Simple Monitoring Quickstart
- Managing Model Versions with Fiddler
- User-defined Feature Impact Upload
- Image/Computer Vision Model Monitoring
- NLP Model Monitoring - Multiclass Classification
- Class Imbalance Drift Detection
- Ranking Model - Monitoring
- Regression Model - Monitoring
The misc-utils directory contains utility notebooks for customer success engineers, field AI engineers, and solution engineers. These notebooks provide tools for various administrative tasks and solutions to common challenges when working with Fiddler deployments. See the misc-utils README for a detailed catalog of available utilities.
This project is licensed under the MIT license. See the LICENSE file for more info.