Adding MLFlow API for creating registered models

What does this MR do and why?

This MR adds the first MLFlow-compatible endpoints for the Registered Models API.

The Create RegisteredModel API accepts a name, descriptions and an array of tags which are key/value pairs. It returns a RegisteredModel object, or an error message if the validation fails.

The Get RegisteredModel API accepts a name and returns a RegisteredModel object, or an error message if the record is not found.

Example response payload:

{
  "name": "my-model-name",
  "creation_timestamp": "2023-10-17T18:26:55.556Z",
  "last_updated_timestamp": "2023-10-17T18:26:55.556Z",
  "description": "My Model Description",
  "user_id": "1",
  "tags": [
    {
      "key": "key1",
      "value": "value1"
    },
    {
      "key": "key2",
      "value": "value2"
    }
  ]
}

How to set up and validate locally

1. In the Rails console, ensure the feature flag is enabled

   Feature.enable(:ml_experiment_tracking)

2. Use the following cURL command to create a model in a local GDK project

   curl -X "POST" "http://GDKHOST/api/v4/projects/PROJECT_ID/ml/mlflow/api/2.0/mlflow/registered-models/create" \
       -H 'Authorization: Bearer PERSONAL_ACCESS_TOKEN' \
       -H 'Content-Type: application/json; charset=utf-8' \
       -d $'{
     "name": "my-model-name",
     "tags": [
       {
         "key": "key1",
         "value": "value1"
       },
       {
         "key": "key2",
         "value": "value2"
       }
     ],
     "description": "My Model Description"
   }'    

3. Use the get API to look up the newly created entry.

   curl -X "GET" "http://GDKHOST/api/v4/projects/PROJECT_ID/ml/mlflow/api/2.0/mlflow/registered-models/get?name=my-model-name" \
       -H 'Authorization: Bearer PERSONAL_ACCESS_TOKEN'    

Database Review

Find Query

SELECT
  "ml_models".*
FROM
  "ml_models"
WHERE
  "ml_models"."project_id" = 37577134
  AND "ml_models"."name" = 'ml_experiment_4'
LIMIT
  1

Create Queries

Steps

1. Find or create a model experiment
2. Find or create a model
3. Insert metadata records

Queries

1. Look up existing model experiments

   SELECT
     1 AS one
   FROM
     "ml_experiments"
   WHERE
     "ml_experiments"."name" = 'my-experiment-name'
     AND "ml_experiments"."project_id" = 37577134
   LIMIT
     1

https://console.postgres.ai/gitlab/gitlab-production-tunnel-pg12/sessions/23291/commands/74953

1. Insert new model experiment

   INSERT INTO
     "ml_experiments" (
       "created_at",
       "updated_at",
       "iid",
       "project_id",
       "name"
     )
   VALUES
     (
       '2023-10-19 20:54:35.627506',
       '2023-10-19 20:54:35.627506',
       99999,
       37577134,
       'my-experiment-name'
     ) RETURNING "id"

https://console.postgres.ai/gitlab/gitlab-production-tunnel-pg12/sessions/23291/commands/74954

1. Look up existing model with given name

   SELECT
     "ml_models".*
   FROM
     "ml_models"
   WHERE
     "ml_models"."project_id" = 37577134
     AND "ml_models"."name" = 'ml_experiment_999'
   LIMIT
     1

https://console.postgres.ai/gitlab/gitlab-production-tunnel-pg12/sessions/23291/commands/74955

1. Insert new model record

   INSERT INTO
     "ml_models" (
       "created_at",
       "updated_at",
       "project_id",
       "name",
       "user_id"
     )
   VALUES
     (
       '2023-10-19 20:54:35.645057',
       '2023-10-19 20:54:35.645057',
       37577134,
       'ml_experiment_999',
       1
     ) RETURNING "id"

https://console.postgres.ai/gitlab/gitlab-production-tunnel-pg12/sessions/23291/commands/74957

1. Insert metadata entries

   INSERT INTO
     "ml_model_metadata" (
       "model_id",
       "name",
       "value",
       "created_at",
       "updated_at"
     )
   VALUES
     (
       13,
       'key1',
       'value1',
       CURRENT_TIMESTAMP,
       CURRENT_TIMESTAMP
     ),
     (
       13,
       'key2',
       'value2',
       CURRENT_TIMESTAMP,
       CURRENT_TIMESTAMP
     ) ON CONFLICT DO NOTHING

MR acceptance checklist

This checklist encourages us to confirm any changes have been analyzed to reduce risks in quality, performance, reliability, security, and maintainability.

• I have evaluated the MR acceptance checklist for this MR.