Co-authored with Elena Apreutesei, Principal Software Engineer, Azure Machine Learning.

The Cortana Analytics Gallery is a community driven website used to discover, share, and learn about solutions built from the Cortana Analytics Suite. The Gallery hosts a wide range of solutions; everything from a Retail Forecasting experiment to the ever popular Face APIs used in the How old do I look? app. Machine Learning enthusiasts can share their own experiments using the Azure Machine Learning Studio, a private space focused on Machine Learning experimentation and model creation.

The Cortana Analytics Gallery follows a micro service architecture with single purpose components working together to deliver reliable and durable functionality. This post focuses on the Gallery Catalog API, which is used as the data master within the Gallery user experience.

*Gallery architecture is subject to change

Gallery Catalog API

Every Gallery entity has a JSON metadata document stored in Azure DocumentDB and all CRUD (create, read, update, and delete) operations are against DocumentDB. As a micro service, the Gallery Catalog API provides REST APIs and supports OData; both of which were built through the ASP.NET library WebApi. Generally, the Catalog API pushes the query filters provided through ODATA directly down to the DocumentDB LINQ provider as an ExpressionTree, where it gets executed at the database.

The Gallery Catalog service exposes GET / POST / PATCH / DELETE operations on standard WebAPI routes, such as the generic route /entities/{entityId} and the specialized routes /experiments/{entityId}, /collections/{entityId}, /tutorials/{entityId}, etc.

The JSON objects in the REST API payload are simply the serialized form of the Catalog entity data contracts, derived from a common class EntityBase which provides the flexibility to store other entities.

    [DataContract]
    public class Experiment : EntityBase
    {
        public Experiment()
        {
            this.EntityType = EntityType.Experiment;
        }

        [DataMember(Name = "modules")]
        public IList Modules { get; set; }
    }

Catalog entities contain the essential metadata, such as name and summary, for user facing context. For example, the JSON object below maps to:

{
  "entity_type": "Experiment",
  "name": "Predict the remaining useful life of an aircraft engine",
  "summary": "This experiment aims to build a regression model to predict the RUL (Remaining Useful Life) of a specific aircraft engine. ",
  "description": "This experiment aims to build a regression model to predict the RUL (Remaining Useful Life) of a specific aircraft engine, including data...",

In addition, entities contain reference links, such as image_url and package_link. The raw image and package data are both stored in Blob Storage.

  "image_url": "https://contentmamluswest001.blob.core.windows.net/content/14b2744cf8d6418c87ffddc3f3127242/9502630827244d60a1214f250e3bbca7/42332590d22a44e5a6e0dac7293e61b3/image",
  "created_at": "2015-02-10T22:25:45.0510842+00:00",
  "updated_at": "2015-02-13T19:25:00+00:00",
  "content": {
    "service_type": "Azure",
    "package_link": "https://storage.azureml.net/directories/f2c4da7bcd4847259f6879a1d4c73889"
  },
  "hidden": false,

Tags and algorithms properties are both arrays of strings, which enables DocumentDB queries to find all Catalog entities related to specific tags and algorithms in real-time.

  "tags": [
    "regression",
    "predictive maintenance",
    "prognostics"
  ],
  "algorithms": [
    "Decision Forest Regression",
    "Boosted Decision Tree Regression"
  ],
  "author": {
    "name": "Yan Zhang",
    "avatar_url": "https://cid-335e4711451a0d9f.users.storage.live.com/users/0x335e4711451a0d9f/myprofile/expressionprofile/profilephoto:UserTileStatic,UserTileMedium,Win8Static",
    "id": "FD02E574E0801687BA770AE1993A458FD3DB7090DBD739116F411328FEA31939"
  },

The entities also contain download_count, view_count, share_count, and trending properties to order returning results based on popularity. The etag property is provided by DocumentDB and used to manage conflicting edits.

  "download_count": 344,
  "view_count": 730,
  "share_count": 1,
  "trending": 12.34544664797978,
  "index_eid": "e9e50264-b77f-4eb0-bc08-62e20f3ac6ed",
  "id": "42332590d22a44e5a6e0dac7293e61b3",
  "_links": {
    "self": "https://catalog.azureml.net/tenants/14b2744cf8d6418c87ffddc3f3127242/communities/9502630827244d60a1214f250e3bbca7/experiments/42332590d22a44e5a6e0dac7293e61b3"
  },
  "etag": ""00003501-0000-0000-0000-563ae96c0000""
}

Sample query used to retrieve all experiments from a specific author:

GET /entities?$orderby=updated_at desc&$filter=author/id eq '151C1FC0FFA9AE788F7872766C4076EA49F8EDD487E674688F98D8F77E22FA4B' and (entity_type eq Microsoft.MachineLearning.Community.Contracts.Catalog.EntityType'Experiment')

Sample query used to get all collections, sorted by trending:

GET /entities?$orderby=trending desc&$filter=(entity_type eq Microsoft.MachineLearning.Community.Contracts.Catalog.EntityType'Collection')

The Gallery Catalog micro service leverages DocumentDB’s schema free nature and LINQ support to build a reliable, flexible, and scalable canonical store for Gallery entities’ metadata.

Next steps

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