Catalog File Specification

The Catalog file is a YAML file that contains descriptive information and metadata for the displaying the AMP projects in the Project Catalog.


Fields are in snake_case. Each project in the catalog uses the following fields:

Field Name




name string name: Cloudera Required. Name of the catalog, displayed as Source in the Prototype Catalog tab.
entries string entries: Required. Contains the entries for each project.
title string title: Churn Modeling Required. The title of the AMP, as displayed in the Prototype Catalog.
label string label: churn-prediction Required.
short_description string short_description: Build an scikit-learn model... Required. A short description of the project. Appears on the project tile in the Prototype Catalog.
long_description string long_description: >- This project demonstrates... Required. A longer description that appears when the user clicks on the project tile.
image_path string image_path: >- https://raw.git... Required. Path to the image file that displays in the Prototype Catalog.
tags string tags: - Churn Prediction - Logistic Regression Required. For sorting in the Prototype Catalog pane.
git_url string git_url: "https:...” Required. Path to the git repository for the project.
is_prototype boolean is_prototype: true Optional. Indicates the AMP should be displayed in the Prototype Catalog. Use if coming_soon is not used.
coming_soon boolean coming_soon: true Optional. Displays the AMP in the Prototype Catalog with a “COMING SOON” watermark. Use if is_prototype is not used.


name: Cloudera
   - title: Churn Modeling with scikit-learn
     label: churn-prediction
     short_description: Build an scikit-learn model to predict churn using customer telco data.
     long_description: >-
     This project demonstrates how to build a logistic regression classification model to predict the probability 
     that a group of customers will churn from a fictitious telecommunications company. In addition, the model is 
     interpreted using a technique called Local Interpretable Model-agnostic Explanations (LIME). Both the logistic 
     regression and LIME models are deployed using CML's real-time model deployment capability and interact with a 
     basic Flask-based web application.
     image_path: >-
      - Churn Prediction
      - Logistic Regression
      - Explainability
      - Lime
    git_url: ""
    is_prototype: true