Introduction
What is Chanterelle?
Chanterelle is a lightweight desktop application designed for data scientists and ML engineers to easily test, present and share models and findings in a structured and interactive way. It allows model developers to:
- 📄 Describe models using structured metadata
- 📊 Show insights and findings through an optional report page
- 🧠 Interact with the model via a user-defined input/output interface
Why use Chanterelle?
- Local, simple, and secure – No need to deploy to the cloud, especially before production.
- Share models easily – Present models, results, and visual findings with minimal setup
- Structure your work – Organize metadata, reports, and interfaces in a clear, reusable format
- Collaborate like code – Project files are JSON + Python, so teams can version them in Git, push to shared repos, and review changes just like with source code
- Integrate with your workflow – Fully compatible with existing Python environments and model pipelines
How It Works
Chanterelle is built around three core components, all configurable by the model developer:
1. Model Metadata
Specify inputs, outputs, and UI settings using a JSON configuration.
2. Model Functions
Write your core Python functions to:
- Load and prepare your model
- Transform inputs
- Run predictions
- Return outputs and, optionally, generate dynamic visualizations or plots
These functions allow Chanterelle to serve as a lightweight local UI for testing and showcasing your model. Preparing these functions also makes it easier to deploy models elsewhere (e.g. AWS SageMaker).
3. Insights & Findings (Optional)
You can include a dedicated page for:
- Model results, KPIs, and benchmark comparisons
- Visual findings like feature importance plots, confusion matrices, partial dependence plots or cohort analyses.
All content is defined via a JSON structure linked to static or dynamic visual outputs.