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

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

Field Name	Type	Example	Description
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.

Example:

name: Cloudera
        
entries:
   - 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: >-
      https://raw.githubusercontent.com/cloudera/Applied-ML-Prototypes/master/images/churn-prediction.jpg
    tags:
      - Churn Prediction
      - Logistic Regression
      - Explainability
      - Lime
    git_url: "https://github.com/cloudera/CML_AMP_Churn_Prediction"
    is_prototype: true