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. |
git_ref |
string |
git_ref: 9e56b6578e37185777380e2a474b3e83d9cc6ac3 |
Optional. Git ref (branch Name /commit hash/tag name). |
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 Cloudera Machine Learning'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