Get Started

First, make sure you have everything set up correctly to run RIME:

• Setup

Then, check out one of our Google Colab tutorials for a quick introduction into running RIME.

Each notebook illustrates how to use RIME to validate, protect, and monitor your ML pipeline across a wide range of tasks (from classification to ranking) and data modalities (Tabular, NLP, and Images). The datasets and models used in these examples come from well-known public sources like UCI ML or arXiv. In these notebooks, we walk through the steps of setting up RIME with an example dataset/model; we run Stress Testing and then the AI Firewall.

Tabular Notebooks

• Fraud Detection (Binary Classification)
• NYC Taxi Dataset (Regression)
• Movie Recommendations (Ranking)

NLP Notebooks

• ArXiv Dataset (NLP, Classification)
• NLP Adversarial Robustness

CV Notebooks

• Animals with Attributes 2 (Images, Classification)

We also provide a lightweight template so that you can easily plug in your own dataset and model.

Custom Notebooks

• Use RIME with your Own Dataset/Model