Challenger models¶
Use the endpoints described below to manage challenger models.
POST /api/v2/deployments/{deploymentId}/challengerPredictions/¶
Score main model prediction requests against challenger model requests.
Body parameter¶
{
"properties": {
"timestamp": {
"description": "The date and time in ISO8601 format, challenger models will be scored on data starting from deployment creation until timestamp. If not specified UTC current time is used.",
"format": "date-time",
"type": "string"
}
},
"type": "object"
}
Parameters
| Name | In | Type | Required | Description |
|---|---|---|---|---|
| deploymentId | path | string | true | Unique identifier of the deployment. |
| body | body | ChallengerScore | false | none |
Responses¶
| Status | Meaning | Description | Schema |
|---|---|---|---|
| 202 | Accepted | Job submitted. See Location header. | None |
| 422 | Unprocessable Entity | Unable to process the challenger scoring request. | None |
| 429 | Too Many Requests | Another challenger scoring job is running. See the Location header to track the running job. | None |
Response Headers¶
| Status | Header | Type | Format | Description |
|---|---|---|---|---|
| 202 | Location | string | URL to poll to track challenger scoring progress. |
To perform this operation, you must be authenticated by means of one of the following methods:
BearerAuth
GET /api/v2/deployments/{deploymentId}/challengerReplaySettings/¶
Retrieve challenger replay settings.
Parameters
| Name | In | Type | Required | Description |
|---|---|---|---|---|
| deploymentId | path | string | true | Unique identifier of the deployment. |
Example responses¶
200 Response
{
"properties": {
"enabled": {
"description": "Identifies whether scheduled replay is enabled.",
"type": "boolean",
"x-versionadded": "v2.33"
},
"schedule": {
"description": "The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False.",
"properties": {
"dayOfMonth": {
"description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.",
"items": {
"enum": [
"*",
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23,
24,
25,
26,
27,
28,
29,
30,
31
],
"type": [
"number",
"string"
]
},
"maxItems": 31,
"type": "array"
},
"dayOfWeek": {
"description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.",
"items": {
"enum": [
"*",
0,
1,
2,
3,
4,
5,
6,
"sunday",
"SUNDAY",
"Sunday",
"monday",
"MONDAY",
"Monday",
"tuesday",
"TUESDAY",
"Tuesday",
"wednesday",
"WEDNESDAY",
"Wednesday",
"thursday",
"THURSDAY",
"Thursday",
"friday",
"FRIDAY",
"Friday",
"saturday",
"SATURDAY",
"Saturday",
"sun",
"SUN",
"Sun",
"mon",
"MON",
"Mon",
"tue",
"TUE",
"Tue",
"wed",
"WED",
"Wed",
"thu",
"THU",
"Thu",
"fri",
"FRI",
"Fri",
"sat",
"SAT",
"Sat"
],
"type": [
"number",
"string"
]
},
"maxItems": 7,
"type": "array"
},
"hour": {
"description": "The hour(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every hour of the day or ``[0 ... 23]``.",
"items": {
"enum": [
"*",
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23
],
"type": [
"number",
"string"
]
},
"maxItems": 24,
"type": "array"
},
"minute": {
"description": "The minute(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every minute of the day or``[0 ... 59]``.",
"items": {
"enum": [
"*",
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23,
24,
25,
26,
27,
28,
29,
30,
31,
32,
33,
34,
35,
36,
37,
38,
39,
40,
41,
42,
43,
44,
45,
46,
47,
48,
49,
50,
51,
52,
53,
54,
55,
56,
57,
58,
59
],
"type": [
"number",
"string"
]
},
"maxItems": 60,
"type": "array"
},
"month": {
"description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.",
"items": {
"enum": [
"*",
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
"january",
"JANUARY",
"January",
"february",
"FEBRUARY",
"February",
"march",
"MARCH",
"March",
"april",
"APRIL",
"April",
"may",
"MAY",
"May",
"june",
"JUNE",
"June",
"july",
"JULY",
"July",
"august",
"AUGUST",
"August",
"september",
"SEPTEMBER",
"September",
"october",
"OCTOBER",
"October",
"november",
"NOVEMBER",
"November",
"december",
"DECEMBER",
"December",
"jan",
"JAN",
"Jan",
"feb",
"FEB",
"Feb",
"mar",
"MAR",
"Mar",
"apr",
"APR",
"Apr",
"jun",
"JUN",
"Jun",
"jul",
"JUL",
"Jul",
"aug",
"AUG",
"Aug",
"sep",
"SEP",
"Sep",
"oct",
"OCT",
"Oct",
"nov",
"NOV",
"Nov",
"dec",
"DEC",
"Dec"
],
"type": [
"number",
"string"
]
},
"maxItems": 12,
"type": "array"
}
},
"required": [
"dayOfMonth",
"dayOfWeek",
"hour",
"minute",
"month"
],
"type": "object"
}
},
"type": "object"
}
Responses¶
| Status | Meaning | Description | Schema |
|---|---|---|---|
| 200 | OK | Challenger replay settings. | ChallengersReplaySettings |
To perform this operation, you must be authenticated by means of one of the following methods:
BearerAuth
PATCH /api/v2/deployments/{deploymentId}/challengerReplaySettings/¶
Update challenger replay settings.
Body parameter¶
{
"properties": {
"enabled": {
"description": "Identifies whether scheduled replay is enabled.",
"type": "boolean",
"x-versionadded": "v2.33"
},
"schedule": {
"description": "The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False.",
"properties": {
"dayOfMonth": {
"description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.",
"items": {
"enum": [
"*",
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23,
24,
25,
26,
27,
28,
29,
30,
31
],
"type": [
"number",
"string"
]
},
"maxItems": 31,
"type": "array"
},
"dayOfWeek": {
"description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.",
"items": {
"enum": [
"*",
0,
1,
2,
3,
4,
5,
6,
"sunday",
"SUNDAY",
"Sunday",
"monday",
"MONDAY",
"Monday",
"tuesday",
"TUESDAY",
"Tuesday",
"wednesday",
"WEDNESDAY",
"Wednesday",
"thursday",
"THURSDAY",
"Thursday",
"friday",
"FRIDAY",
"Friday",
"saturday",
"SATURDAY",
"Saturday",
"sun",
"SUN",
"Sun",
"mon",
"MON",
"Mon",
"tue",
"TUE",
"Tue",
"wed",
"WED",
"Wed",
"thu",
"THU",
"Thu",
"fri",
"FRI",
"Fri",
"sat",
"SAT",
"Sat"
],
"type": [
"number",
"string"
]
},
"maxItems": 7,
"type": "array"
},
"hour": {
"description": "The hour(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every hour of the day or ``[0 ... 23]``.",
"items": {
"enum": [
"*",
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23
],
"type": [
"number",
"string"
]
},
"maxItems": 24,
"type": "array"
},
"minute": {
"description": "The minute(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every minute of the day or``[0 ... 59]``.",
"items": {
"enum": [
"*",
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23,
24,
25,
26,
27,
28,
29,
30,
31,
32,
33,
34,
35,
36,
37,
38,
39,
40,
41,
42,
43,
44,
45,
46,
47,
48,
49,
50,
51,
52,
53,
54,
55,
56,
57,
58,
59
],
"type": [
"number",
"string"
]
},
"maxItems": 60,
"type": "array"
},
"month": {
"description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.",
"items": {
"enum": [
"*",
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
"january",
"JANUARY",
"January",
"february",
"FEBRUARY",
"February",
"march",
"MARCH",
"March",
"april",
"APRIL",
"April",
"may",
"MAY",
"May",
"june",
"JUNE",
"June",
"july",
"JULY",
"July",
"august",
"AUGUST",
"August",
"september",
"SEPTEMBER",
"September",
"october",
"OCTOBER",
"October",
"november",
"NOVEMBER",
"November",
"december",
"DECEMBER",
"December",
"jan",
"JAN",
"Jan",
"feb",
"FEB",
"Feb",
"mar",
"MAR",
"Mar",
"apr",
"APR",
"Apr",
"jun",
"JUN",
"Jun",
"jul",
"JUL",
"Jul",
"aug",
"AUG",
"Aug",
"sep",
"SEP",
"Sep",
"oct",
"OCT",
"Oct",
"nov",
"NOV",
"Nov",
"dec",
"DEC",
"Dec"
],
"type": [
"number",
"string"
]
},
"maxItems": 12,
"type": "array"
}
},
"required": [
"dayOfMonth",
"dayOfWeek",
"hour",
"minute",
"month"
],
"type": "object"
}
},
"type": "object"
}
Parameters
| Name | In | Type | Required | Description |
|---|---|---|---|---|
| deploymentId | path | string | true | Unique identifier of the deployment. |
| body | body | ChallengersReplaySettings | false | none |
Responses¶
| Status | Meaning | Description | Schema |
|---|---|---|---|
| 200 | OK | Challenger replay settings updated. | None |
To perform this operation, you must be authenticated by means of one of the following methods:
BearerAuth
GET /api/v2/deployments/{deploymentId}/challengers/¶
List challenger models for deployment.
Parameters
| Name | In | Type | Required | Description |
|---|---|---|---|---|
| deploymentId | path | string | true | Unique identifier of the deployment. |
Example responses¶
200 Response
{
"properties": {
"data": {
"description": "List of challengers.",
"items": {
"properties": {
"id": {
"description": "ID of the challenger.",
"type": "string"
},
"model": {
"description": "Model of the challenger.",
"properties": {
"datasetName": {
"description": "Name of dataset used to train challenger model",
"type": "string"
},
"description": {
"description": "Description of the model.",
"type": "string"
},
"executionType": {
"description": "Type of the current model.",
"type": "string"
},
"id": {
"description": "ID of the current model.",
"type": "string"
},
"isDeprecated": {
"description": "Whether the current model is deprecated model. eg. python2 based model.",
"type": "boolean",
"x-versionadded": "v2.29"
},
"name": {
"description": "Name of the model.",
"type": "string"
},
"projectId": {
"description": "Project ID of the current model.",
"type": "string"
},
"projectName": {
"description": "Project name of the current model.",
"type": "string"
}
},
"required": [
"datasetName",
"description",
"executionType",
"id",
"isDeprecated",
"name",
"projectId",
"projectName"
],
"type": "object"
},
"modelPackage": {
"description": "modelPackage of the challenger.",
"properties": {
"id": {
"description": "ID of the model package.",
"type": "string"
},
"name": {
"description": "Type of the current model.",
"type": "string"
},
"registeredModelId": {
"description": "ID of the associated registered model",
"type": [
"string",
"null"
]
}
},
"required": [
"id",
"name"
],
"type": "object"
},
"name": {
"description": "Name of the challenger.",
"type": "string"
},
"predictionEnvironment": {
"description": "Prediction environment used by the challenger",
"properties": {
"id": {
"description": "ID of the prediction environment.",
"type": "string"
},
"name": {
"description": "Name of the prediction environment.",
"type": "string"
}
},
"required": [
"id",
"name"
],
"type": "object"
}
},
"required": [
"id",
"model",
"modelPackage",
"name"
],
"type": "object"
},
"type": "array"
}
},
"required": [
"data"
],
"type": "object"
}
Responses¶
| Status | Meaning | Description | Schema |
|---|---|---|---|
| 200 | OK | The challenger models | ChallengerListResponse |
To perform this operation, you must be authenticated by means of one of the following methods:
BearerAuth
POST /api/v2/deployments/{deploymentId}/challengers/¶
Create new challenger model.
Body parameter¶
{
"properties": {
"modelPackageId": {
"description": "ID of the model package to add as a challenger.",
"type": "string"
},
"name": {
"description": "Human-readable name for the challenger.",
"maxLength": 512,
"type": "string"
},
"predictionEnvironmentId": {
"description": "ID of the Prediction Environment the challenger should use. If prediction environments are enabled, this is required",
"type": "string"
}
},
"required": [
"modelPackageId",
"name"
],
"type": "object"
}
Parameters
| Name | In | Type | Required | Description |
|---|---|---|---|---|
| deploymentId | path | string | true | Unique identifier of the deployment. |
| body | body | ChallengerCreate | false | none |
Responses¶
| Status | Meaning | Description | Schema |
|---|---|---|---|
| 202 | Accepted | Job submitted successfully. See Location header. | None |
| 422 | Unprocessable Entity | Unable to process the challenger creation request. | None |
Response Headers¶
| Status | Header | Type | Format | Description |
|---|---|---|---|---|
| 202 | Location | string | URL to poll to track challenger creation has finished. |
To perform this operation, you must be authenticated by means of one of the following methods:
BearerAuth
DELETE /api/v2/deployments/{deploymentId}/challengers/{challengerId}/¶
Delete challenger model.
Parameters
| Name | In | Type | Required | Description |
|---|---|---|---|---|
| deploymentId | path | string | true | Unique identifier of the deployment. |
| challengerId | path | string | true | Unique identifier of the challenger. |
Responses¶
| Status | Meaning | Description | Schema |
|---|---|---|---|
| 204 | No Content | Model successfully deleted. | None |
To perform this operation, you must be authenticated by means of one of the following methods:
BearerAuth
GET /api/v2/deployments/{deploymentId}/challengers/{challengerId}/¶
Retrieve challenger model.
Parameters
| Name | In | Type | Required | Description |
|---|---|---|---|---|
| deploymentId | path | string | true | Unique identifier of the deployment. |
| challengerId | path | string | true | Unique identifier of the challenger. |
Example responses¶
200 Response
{
"properties": {
"id": {
"description": "ID of the challenger.",
"type": "string"
},
"model": {
"description": "Model of the challenger.",
"properties": {
"datasetName": {
"description": "Name of dataset used to train challenger model",
"type": "string"
},
"description": {
"description": "Description of the model.",
"type": "string"
},
"executionType": {
"description": "Type of the current model.",
"type": "string"
},
"id": {
"description": "ID of the current model.",
"type": "string"
},
"isDeprecated": {
"description": "Whether the current model is deprecated model. eg. python2 based model.",
"type": "boolean",
"x-versionadded": "v2.29"
},
"name": {
"description": "Name of the model.",
"type": "string"
},
"projectId": {
"description": "Project ID of the current model.",
"type": "string"
},
"projectName": {
"description": "Project name of the current model.",
"type": "string"
}
},
"required": [
"datasetName",
"description",
"executionType",
"id",
"isDeprecated",
"name",
"projectId",
"projectName"
],
"type": "object"
},
"modelPackage": {
"description": "modelPackage of the challenger.",
"properties": {
"id": {
"description": "ID of the model package.",
"type": "string"
},
"name": {
"description": "Type of the current model.",
"type": "string"
},
"registeredModelId": {
"description": "ID of the associated registered model",
"type": [
"string",
"null"
]
}
},
"required": [
"id",
"name"
],
"type": "object"
},
"name": {
"description": "Name of the challenger.",
"type": "string"
},
"predictionEnvironment": {
"description": "Prediction environment used by the challenger",
"properties": {
"id": {
"description": "ID of the prediction environment.",
"type": "string"
},
"name": {
"description": "Name of the prediction environment.",
"type": "string"
}
},
"required": [
"id",
"name"
],
"type": "object"
}
},
"required": [
"id",
"model",
"modelPackage",
"name"
],
"type": "object"
}
Responses¶
| Status | Meaning | Description | Schema |
|---|---|---|---|
| 200 | OK | The challenger model | ChallengerResponse |
To perform this operation, you must be authenticated by means of one of the following methods:
BearerAuth
PATCH /api/v2/deployments/{deploymentId}/challengers/{challengerId}/¶
Update challenger model.
Body parameter¶
{
"properties": {
"name": {
"description": "Human-readable name for the challenger.",
"maxLength": 512,
"type": "string"
},
"predictionEnvironmentId": {
"description": "ID of the Prediction Environment the challenger should use.",
"type": "string"
}
},
"type": "object"
}
Parameters
| Name | In | Type | Required | Description |
|---|---|---|---|---|
| deploymentId | path | string | true | Unique identifier of the deployment. |
| challengerId | path | string | true | Unique identifier of the challenger. |
| body | body | ChallengerUpdate | false | none |
Example responses¶
200 Response
{
"properties": {
"id": {
"description": "ID of the challenger.",
"type": "string"
},
"model": {
"description": "Model of the challenger.",
"properties": {
"datasetName": {
"description": "Name of dataset used to train challenger model",
"type": "string"
},
"description": {
"description": "Description of the model.",
"type": "string"
},
"executionType": {
"description": "Type of the current model.",
"type": "string"
},
"id": {
"description": "ID of the current model.",
"type": "string"
},
"isDeprecated": {
"description": "Whether the current model is deprecated model. eg. python2 based model.",
"type": "boolean",
"x-versionadded": "v2.29"
},
"name": {
"description": "Name of the model.",
"type": "string"
},
"projectId": {
"description": "Project ID of the current model.",
"type": "string"
},
"projectName": {
"description": "Project name of the current model.",
"type": "string"
}
},
"required": [
"datasetName",
"description",
"executionType",
"id",
"isDeprecated",
"name",
"projectId",
"projectName"
],
"type": "object"
},
"modelPackage": {
"description": "modelPackage of the challenger.",
"properties": {
"id": {
"description": "ID of the model package.",
"type": "string"
},
"name": {
"description": "Type of the current model.",
"type": "string"
},
"registeredModelId": {
"description": "ID of the associated registered model",
"type": [
"string",
"null"
]
}
},
"required": [
"id",
"name"
],
"type": "object"
},
"name": {
"description": "Name of the challenger.",
"type": "string"
},
"predictionEnvironment": {
"description": "Prediction environment used by the challenger",
"properties": {
"id": {
"description": "ID of the prediction environment.",
"type": "string"
},
"name": {
"description": "Name of the prediction environment.",
"type": "string"
}
},
"required": [
"id",
"name"
],
"type": "object"
}
},
"required": [
"id",
"model",
"modelPackage",
"name"
],
"type": "object"
}
Responses¶
| Status | Meaning | Description | Schema |
|---|---|---|---|
| 200 | OK | Model successfully updated. | ChallengerResponse |
To perform this operation, you must be authenticated by means of one of the following methods:
BearerAuth
GET /api/v2/deployments/{deploymentId}/championModelPackage/¶
Retrieve information about the champion model package
Parameters
| Name | In | Type | Required | Description |
|---|---|---|---|---|
| deploymentId | path | string | true | Unique identifier of the deployment. |
Example responses¶
200 Response
{
"properties": {
"buildStatus": {
"description": "Model package build status",
"enum": [
"inProgress",
"complete",
"failed"
],
"type": [
"string",
"null"
],
"x-versionadded": "v2.33"
},
"capabilities": {
"description": "Capabilities of the current model package.",
"properties": {
"supportsAutomaticActuals": {
"description": "Whether inferring actual values from time series history data and automatically feeding them back for accuracy estimation is supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2",
"x-versiondeprecated": "v2.29"
},
"supportsChallengerModels": {
"description": "Whether Challenger Models are supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2",
"x-versiondeprecated": "v2.29"
},
"supportsFeatureDriftTracking": {
"description": "Whether Feature Drift is supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2",
"x-versiondeprecated": "v2.29"
},
"supportsHumilityRecommendedRules": {
"description": "Whether calculating values for recommended Humility Rules is supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2",
"x-versiondeprecated": "v2.29"
},
"supportsHumilityRules": {
"description": "Whether Humility Rules are supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2",
"x-versiondeprecated": "v2.29"
},
"supportsHumilityRulesDefaultCalculations": {
"description": "Whether calculating default values for Humility Rules is supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2"
},
"supportsPredictionWarning": {
"description": "Whether Prediction Warnings are supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2",
"x-versiondeprecated": "v2.29"
},
"supportsRetraining": {
"description": "Whether deployment supports retraining.",
"type": "boolean",
"x-versionadded": "v2.28",
"x-versiondeprecated": "v2.29"
},
"supportsScoringCodeDownload": {
"description": "Whether scoring code download is supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2",
"x-versiondeprecated": "v2.29"
},
"supportsSecondaryDatasets": {
"description": "If the deployments supports secondary datasets.",
"type": "boolean",
"x-versionadded": "v2.28",
"x-versiondeprecated": "v2.29"
},
"supportsSegmentedAnalysisDriftAndAccuracy": {
"description": "Whether tracking features in training and predictions data for segmented analysis is supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2",
"x-versiondeprecated": "v2.29"
},
"supportsShapBasedPredictionExplanations": {
"description": "Whether shap-based prediction explanations are supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2",
"x-versiondeprecated": "v2.29"
},
"supportsTargetDriftTracking": {
"description": "Whether Target Drift is supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2",
"x-versiondeprecated": "v2.29"
}
},
"required": [
"supportsChallengerModels",
"supportsFeatureDriftTracking",
"supportsHumilityRecommendedRules",
"supportsHumilityRules",
"supportsHumilityRulesDefaultCalculations",
"supportsPredictionWarning",
"supportsSecondaryDatasets",
"supportsSegmentedAnalysisDriftAndAccuracy",
"supportsShapBasedPredictionExplanations",
"supportsTargetDriftTracking"
],
"type": "object"
},
"datasets": {
"description": "dataset information for the model package",
"properties": {
"baselineSegmentedBy": {
"description": "Names of categorical features by which the training baseline was segmented. This allows for deployment prediction requests to be segmented by those same features. Segmenting the training baseline by these features allows for users to perform segmented analysis of Data Drift and Accuracy, and to compare the same subset of training and scoring data based on the selected segment attribute and segment value.",
"items": {
"type": "string"
},
"type": "array"
},
"datasetName": {
"description": "Name of dataset used to train the model",
"type": [
"string",
"null"
]
},
"holdoutDataCatalogId": {
"description": "ID for holdout data (returned from uploading a data set)",
"type": [
"string",
"null"
]
},
"holdoutDataCatalogVersionId": {
"description": "Version ID for holdout data (returned from uploading a data set)",
"type": [
"string",
"null"
]
},
"holdoutDataCreatedAt": {
"description": "Time when the holdout data item was created",
"type": [
"string",
"null"
],
"x-versionadded": "v2.34"
},
"holdoutDataCreatorEmail": {
"description": "Email of the user who created the holdout data item",
"type": [
"string",
"null"
],
"x-versionadded": "v2.34"
},
"holdoutDataCreatorId": {
"default": null,
"description": "ID of the creator of the holdout data item",
"type": [
"string",
"null"
],
"x-versionadded": "v2.34"
},
"holdoutDataCreatorName": {
"description": "Name of the user who created the holdout data item",
"type": [
"string",
"null"
],
"x-versionadded": "v2.34"
},
"holdoutDatasetName": {
"description": "Name of dataset used for model holdout",
"type": [
"string",
"null"
]
},
"targetHistogramBaseline": {
"description": "Values used to establish the training baseline",
"enum": [
"predictions",
"actuals"
],
"type": "string"
},
"trainingDataCatalogId": {
"description": "ID for training data (returned from uploading a data set)",
"type": [
"string",
"null"
]
},
"trainingDataCatalogVersionId": {
"description": "Version ID for training data (returned from uploading a data set)",
"type": [
"string",
"null"
]
},
"trainingDataCreatedAt": {
"description": "Time when the training data item was created",
"type": [
"string",
"null"
],
"x-versionadded": "v2.34"
},
"trainingDataCreatorEmail": {
"description": "Email of the user who created the training data item",
"type": [
"string",
"null"
],
"x-versionadded": "v2.34"
},
"trainingDataCreatorId": {
"default": null,
"description": "ID of the creator of the training data item",
"type": [
"string",
"null"
],
"x-versionadded": "v2.34"
},
"trainingDataCreatorName": {
"description": "Name of the user who created the training data item",
"type": [
"string",
"null"
],
"x-versionadded": "v2.34"
},
"trainingDataSize": {
"description": "Number of rows in training data (used by DR models)",
"type": "integer"
}
},
"required": [
"baselineSegmentedBy",
"datasetName",
"holdoutDataCatalogId",
"holdoutDataCatalogVersionId",
"holdoutDatasetName",
"trainingDataCatalogId",
"trainingDataCatalogVersionId"
],
"type": "object"
},
"id": {
"description": "ID of the Model package",
"type": "string",
"x-versionadded": "v2.33"
},
"importMeta": {
"description": "Information from when this Model Package was first saved",
"properties": {
"containsFearPipeline": {
"description": "Exists for imported models only, indicates thatmodel package contains file with fear pipeline.",
"type": [
"boolean",
"null"
]
},
"containsFeaturelists": {
"description": "Exists for imported models only, indicates thatmodel package contains file with featurelists.",
"type": [
"boolean",
"null"
]
},
"containsLeaderboardMeta": {
"description": "Exists for imported models only, indicates thatmodel package contains file with leaderboard meta.",
"type": [
"boolean",
"null"
]
},
"containsProjectMeta": {
"description": "Exists for imported models only, indicates thatmodel package contains file with project meta.",
"type": [
"boolean",
"null"
]
},
"creatorFullName": {
"description": "Full name of the person who created this model package",
"type": [
"string",
"null"
]
},
"creatorId": {
"description": "User ID of the person who created this Model Package",
"type": "string"
},
"creatorUsername": {
"description": "Username of the person who created this model package",
"type": "string"
},
"dateCreated": {
"description": "When this Model Package was created",
"type": "string"
},
"originalFileName": {
"description": "Exists for imported models only, the original file name that was uploaded",
"type": [
"string",
"null"
]
}
},
"required": [
"creatorFullName",
"creatorId",
"creatorUsername",
"dateCreated",
"originalFileName"
],
"type": "object"
},
"isArchived": {
"description": "Whether the model package is permanently archived (cannot be used in deployment or replacement)",
"type": "boolean",
"x-versionadded": "v2.33"
},
"isDeprecated": {
"description": "Whether the model package is deprecated. eg. python2 models are deprecated.",
"type": "boolean",
"x-versionadded": "v2.29"
},
"mlpkgFileContents": {
"description": "Information about the content of .mlpkg artifact",
"properties": {
"allTimeSeriesPredictionIntervals": {
"description": "Whether .mlpkg contains TS prediction intervals computed for all percentiles",
"type": [
"boolean",
"null"
],
"x-versionadded": "v2.31"
}
},
"type": "object"
},
"modelDescription": {
"description": "model description information for the model package",
"properties": {
"buildEnvironmentType": {
"description": "build environment type of the model",
"enum": [
"DataRobot",
"Python",
"R",
"Java",
"Other"
],
"type": "string"
},
"description": {
"description": "a description of the model",
"type": [
"string",
"null"
]
},
"location": {
"description": "location of the model",
"type": [
"string",
"null"
]
},
"modelCreatedAt": {
"description": "time when the model was created",
"type": [
"string",
"null"
]
},
"modelCreatorEmail": {
"description": "email of the user who created the model",
"type": [
"string",
"null"
]
},
"modelCreatorId": {
"default": null,
"description": "ID of the creator of the model",
"type": [
"string",
"null"
]
},
"modelCreatorName": {
"description": "name of the user who created the model",
"type": [
"string",
"null"
]
},
"modelName": {
"description": "model name",
"type": "string"
}
},
"required": [
"buildEnvironmentType",
"description",
"location"
],
"type": "object"
},
"modelExecutionType": {
"description": "Type of model package. `dedicated` (native DataRobot models) and `custom_inference_model` (user added inference models) both execute on DataRobot prediction servers, `external` do not",
"enum": [
"dedicated",
"custom_inference_model",
"external"
],
"type": "string",
"x-versionadded": "v2.33"
},
"modelId": {
"description": "ID of the model",
"type": "string",
"x-versionadded": "v2.33"
},
"modelKind": {
"description": "Model attribute information",
"properties": {
"isAnomalyDetectionModel": {
"description": "true if this is an anomaly detection model",
"type": "boolean"
},
"isCombinedModel": {
"description": "true if model is a combined model",
"type": "boolean",
"x-versionadded": "v2.27"
},
"isFeatureDiscovery": {
"description": "true if this model uses the Feature Discovery feature",
"type": "boolean"
},
"isMultiseries": {
"description": "true if model is multiseries",
"type": "boolean"
},
"isTimeSeries": {
"description": "true if model is time series",
"type": "boolean"
},
"isUnsupervisedLearning": {
"description": "true if model used unsupervised learning",
"type": "boolean"
}
},
"required": [
"isAnomalyDetectionModel",
"isCombinedModel",
"isFeatureDiscovery",
"isMultiseries",
"isTimeSeries",
"isUnsupervisedLearning"
],
"type": "object"
},
"name": {
"description": "Model package name",
"type": "string",
"x-versionadded": "v2.33"
},
"sourceMeta": {
"description": "Meta information from where this model was generated",
"properties": {
"customModelDetails": {
"description": "Details of the custom model associated to this registered model version",
"properties": {
"createdAt": {
"description": "Time when the custom model was created",
"type": "string"
},
"creatorEmail": {
"description": "Email of the user who created the custom model",
"type": [
"string",
"null"
]
},
"creatorId": {
"description": "ID of the creator of the custom model",
"type": "string"
},
"creatorName": {
"description": "Name of the user who created the custom model",
"type": [
"string",
"null"
]
},
"id": {
"description": "ID of the associated custom model",
"type": "string"
},
"versionLabel": {
"description": "Label of associated custom model version.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.34"
}
},
"required": [
"createdAt",
"creatorId",
"id"
],
"type": "object"
},
"environmentUrl": {
"description": "If available, URL of the source model",
"format": "uri",
"type": [
"string",
"null"
]
},
"fips_140_2Enabled": {
"description": "true if the model was built with FIPS-140-2",
"type": "boolean"
},
"projectCreatedAt": {
"description": "If available, time when the project was created",
"type": [
"string",
"null"
]
},
"projectCreatorEmail": {
"description": "If available, email of the user who created the project",
"type": [
"string",
"null"
]
},
"projectCreatorId": {
"default": null,
"description": "If available, ID of the creator of the project",
"type": [
"string",
"null"
]
},
"projectCreatorName": {
"description": "If available, name of the user who created the project",
"type": [
"string",
"null"
]
},
"projectId": {
"description": "If available, the project id used for this model",
"type": [
"string",
"null"
]
},
"projectName": {
"description": "If available, the project name for this model",
"type": [
"string",
"null"
]
},
"scoringCode": {
"description": "If available, information about the model's scoring code",
"properties": {
"dataRobotPredictionVersion": {
"description": "DataRobot prediction API version for the scoring code",
"type": [
"string",
"null"
]
},
"location": {
"description": "Location of the scoring code",
"enum": [
"local_leaderboard",
"mlpkg"
],
"type": [
"string",
"null"
]
}
},
"required": [
"dataRobotPredictionVersion",
"location"
],
"type": "object"
},
"useCaseDetails": {
"description": "Details of the use-case associated to this registered model version",
"properties": {
"createdAt": {
"description": "Time when use-case was created",
"type": "string"
},
"creatorEmail": {
"description": "Email of the user who created use-case",
"type": [
"string",
"null"
]
},
"creatorId": {
"description": "ID of the creator of the use-case",
"type": "string"
},
"creatorName": {
"description": "Name of the user who created use-case",
"type": [
"string",
"null"
]
},
"id": {
"description": "ID of the associated use-case",
"type": "string"
},
"name": {
"description": "Name of the use case at the moment of creation",
"type": [
"string",
"null"
]
}
},
"required": [
"createdAt",
"creatorId",
"id"
],
"type": "object"
}
},
"required": [
"environmentUrl",
"projectId",
"projectName",
"scoringCode"
],
"type": "object"
},
"target": {
"description": "target information for the model package",
"properties": {
"classCount": {
"description": "Number of classes for classification models.",
"minimum": 0,
"type": [
"integer",
"null"
]
},
"classNames": {
"description": "Class names for prediction results. When target type is Binary, two class names are returned. The first element is the minority (positive) class and the second element is the majority (negative) class. Limited to 100 returned for Multiclass.",
"items": {
"type": "string"
},
"maxItems": 100,
"type": "array"
},
"name": {
"description": "name of the target column",
"type": "string"
},
"predictionProbabilitiesColumn": {
"description": "Field or column name containing prediction probabilities",
"type": [
"string",
"null"
]
},
"predictionThreshold": {
"description": "Prediction threshold used for binary classification models",
"maximum": 1,
"minimum": 0,
"type": [
"number",
"null"
]
},
"type": {
"description": "Target type of the model.",
"enum": [
"Binary",
"Regression",
"Multiclass",
"Multilabel",
"TextGeneration",
"GeoPoint",
"AgenticWorkflow"
],
"type": "string"
}
},
"required": [
"classCount",
"classNames",
"name",
"predictionProbabilitiesColumn",
"predictionThreshold",
"type"
],
"type": "object"
},
"timeseries": {
"description": "time series information for the model package",
"properties": {
"datetimeColumnFormat": {
"description": "Date format for forecast date and forecast point column",
"type": [
"string",
"null"
]
},
"datetimeColumnName": {
"description": "Name of the forecast date column",
"type": [
"string",
"null"
]
},
"effectiveFeatureDerivationWindowEnd": {
"description": "Same concept as `featureDerivationWindowEnd` which is chosen by the user and based on the initial sampled data from the eda sample. When the dataset goes through aim, the pipeline reads the full dataset and figures out the \"real\" FDW (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW.",
"maximum": 0,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.25"
},
"effectiveFeatureDerivationWindowStart": {
"description": "Same concept as `featureDerivationWindowStart` which is chosen by the user and based on the initial sampled data from the eda sample. When the dataset goes through aim, the pipeline reads the full dataset and figures out the \"real\" FDW (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW.",
"maximum": 0,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.25"
},
"featureDerivationWindowEnd": {
"description": "Negative number or zero defining how many time units of the forecast distances time unit into the past relative to the forecast point the feature derivation window should end.",
"maximum": 0,
"type": [
"integer",
"null"
]
},
"featureDerivationWindowStart": {
"description": "Negative number or zero defining how many time units of the forecast distances time unit into the past relative to the forecast point the feature derivation window should begin.",
"maximum": 0,
"type": [
"integer",
"null"
]
},
"forecastDistanceColumnName": {
"description": "Name of the forecast distance column",
"type": [
"string",
"null"
]
},
"forecastDistances": {
"description": "List of integer forecast distances",
"items": {
"type": "integer"
},
"type": "array"
},
"forecastDistancesTimeUnit": {
"description": "The time unit of forecast distances",
"enum": [
"MICROSECOND",
"MILLISECOND",
"SECOND",
"MINUTE",
"HOUR",
"DAY",
"WEEK",
"MONTH",
"QUARTER",
"YEAR"
],
"type": [
"string",
"null"
]
},
"forecastPointColumnName": {
"description": "Name of the forecast point column",
"type": [
"string",
"null"
]
},
"isCrossSeries": {
"description": "true if the model is cross-series.",
"type": [
"boolean",
"null"
]
},
"isNewSeriesSupport": {
"description": "true if the model is optimized to support new series.",
"type": [
"boolean",
"null"
],
"x-versionadded": "v2.25"
},
"isTraditionalTimeSeries": {
"description": "true if the model is traditional time series.",
"type": [
"boolean",
"null"
]
},
"seriesColumnName": {
"description": "Name of the series column in case of multi-series date",
"type": [
"string",
"null"
]
}
},
"required": [
"datetimeColumnFormat",
"datetimeColumnName",
"effectiveFeatureDerivationWindowEnd",
"effectiveFeatureDerivationWindowStart",
"featureDerivationWindowEnd",
"featureDerivationWindowStart",
"forecastDistanceColumnName",
"forecastDistances",
"forecastDistancesTimeUnit",
"forecastPointColumnName",
"isCrossSeries",
"isNewSeriesSupport",
"isTraditionalTimeSeries",
"seriesColumnName"
],
"type": "object"
},
"updatedBy": {
"description": "Information on the user who last modified the registered model",
"properties": {
"email": {
"description": "Email of the user",
"type": [
"string",
"null"
]
},
"id": {
"description": "ID of the user",
"type": "string"
},
"name": {
"description": "Full name of the user",
"type": [
"string",
"null"
]
}
},
"required": [
"email",
"id",
"name"
],
"type": "object"
},
"userProvidedId": {
"description": "A user-provided unique ID associated with the given custom inference model.",
"type": "string",
"x-versionadded": "v2.33"
}
},
"required": [
"capabilities",
"datasets",
"id",
"importMeta",
"isArchived",
"isDeprecated",
"modelDescription",
"modelExecutionType",
"modelId",
"modelKind",
"name",
"sourceMeta",
"target",
"timeseries",
"updatedBy"
],
"type": "object"
}
Responses¶
| Status | Meaning | Description | Schema |
|---|---|---|---|
| 200 | OK | Information retrieved successfully. | ModelPackageRetrieveResponseBase |
To perform this operation, you must be authenticated by means of one of the following methods:
BearerAuth
Schemas¶
ChallengerCreate
{
"properties": {
"modelPackageId": {
"description": "ID of the model package to add as a challenger.",
"type": "string"
},
"name": {
"description": "Human-readable name for the challenger.",
"maxLength": 512,
"type": "string"
},
"predictionEnvironmentId": {
"description": "ID of the Prediction Environment the challenger should use. If prediction environments are enabled, this is required",
"type": "string"
}
},
"required": [
"modelPackageId",
"name"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| modelPackageId | string | true | ID of the model package to add as a challenger. | |
| name | string | true | maxLength: 512 |
Human-readable name for the challenger. |
| predictionEnvironmentId | string | false | ID of the Prediction Environment the challenger should use. If prediction environments are enabled, this is required |
ChallengerListResponse
{
"properties": {
"data": {
"description": "List of challengers.",
"items": {
"properties": {
"id": {
"description": "ID of the challenger.",
"type": "string"
},
"model": {
"description": "Model of the challenger.",
"properties": {
"datasetName": {
"description": "Name of dataset used to train challenger model",
"type": "string"
},
"description": {
"description": "Description of the model.",
"type": "string"
},
"executionType": {
"description": "Type of the current model.",
"type": "string"
},
"id": {
"description": "ID of the current model.",
"type": "string"
},
"isDeprecated": {
"description": "Whether the current model is deprecated model. eg. python2 based model.",
"type": "boolean",
"x-versionadded": "v2.29"
},
"name": {
"description": "Name of the model.",
"type": "string"
},
"projectId": {
"description": "Project ID of the current model.",
"type": "string"
},
"projectName": {
"description": "Project name of the current model.",
"type": "string"
}
},
"required": [
"datasetName",
"description",
"executionType",
"id",
"isDeprecated",
"name",
"projectId",
"projectName"
],
"type": "object"
},
"modelPackage": {
"description": "modelPackage of the challenger.",
"properties": {
"id": {
"description": "ID of the model package.",
"type": "string"
},
"name": {
"description": "Type of the current model.",
"type": "string"
},
"registeredModelId": {
"description": "ID of the associated registered model",
"type": [
"string",
"null"
]
}
},
"required": [
"id",
"name"
],
"type": "object"
},
"name": {
"description": "Name of the challenger.",
"type": "string"
},
"predictionEnvironment": {
"description": "Prediction environment used by the challenger",
"properties": {
"id": {
"description": "ID of the prediction environment.",
"type": "string"
},
"name": {
"description": "Name of the prediction environment.",
"type": "string"
}
},
"required": [
"id",
"name"
],
"type": "object"
}
},
"required": [
"id",
"model",
"modelPackage",
"name"
],
"type": "object"
},
"type": "array"
}
},
"required": [
"data"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| data | [ChallengerResponse] | true | List of challengers. |
ChallengerResponse
{
"properties": {
"id": {
"description": "ID of the challenger.",
"type": "string"
},
"model": {
"description": "Model of the challenger.",
"properties": {
"datasetName": {
"description": "Name of dataset used to train challenger model",
"type": "string"
},
"description": {
"description": "Description of the model.",
"type": "string"
},
"executionType": {
"description": "Type of the current model.",
"type": "string"
},
"id": {
"description": "ID of the current model.",
"type": "string"
},
"isDeprecated": {
"description": "Whether the current model is deprecated model. eg. python2 based model.",
"type": "boolean",
"x-versionadded": "v2.29"
},
"name": {
"description": "Name of the model.",
"type": "string"
},
"projectId": {
"description": "Project ID of the current model.",
"type": "string"
},
"projectName": {
"description": "Project name of the current model.",
"type": "string"
}
},
"required": [
"datasetName",
"description",
"executionType",
"id",
"isDeprecated",
"name",
"projectId",
"projectName"
],
"type": "object"
},
"modelPackage": {
"description": "modelPackage of the challenger.",
"properties": {
"id": {
"description": "ID of the model package.",
"type": "string"
},
"name": {
"description": "Type of the current model.",
"type": "string"
},
"registeredModelId": {
"description": "ID of the associated registered model",
"type": [
"string",
"null"
]
}
},
"required": [
"id",
"name"
],
"type": "object"
},
"name": {
"description": "Name of the challenger.",
"type": "string"
},
"predictionEnvironment": {
"description": "Prediction environment used by the challenger",
"properties": {
"id": {
"description": "ID of the prediction environment.",
"type": "string"
},
"name": {
"description": "Name of the prediction environment.",
"type": "string"
}
},
"required": [
"id",
"name"
],
"type": "object"
}
},
"required": [
"id",
"model",
"modelPackage",
"name"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| id | string | true | ID of the challenger. | |
| model | ModelResponse | true | Model of the challenger. | |
| modelPackage | ModelPackageResponse | true | modelPackage of the challenger. | |
| name | string | true | Name of the challenger. | |
| predictionEnvironment | PredictionEnvironmentResponse | false | Prediction environment used by the challenger |
ChallengerScore
{
"properties": {
"timestamp": {
"description": "The date and time in ISO8601 format, challenger models will be scored on data starting from deployment creation until timestamp. If not specified UTC current time is used.",
"format": "date-time",
"type": "string"
}
},
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| timestamp | string(date-time) | false | The date and time in ISO8601 format, challenger models will be scored on data starting from deployment creation until timestamp. If not specified UTC current time is used. |
ChallengerUpdate
{
"properties": {
"name": {
"description": "Human-readable name for the challenger.",
"maxLength": 512,
"type": "string"
},
"predictionEnvironmentId": {
"description": "ID of the Prediction Environment the challenger should use.",
"type": "string"
}
},
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| name | string | false | maxLength: 512 |
Human-readable name for the challenger. |
| predictionEnvironmentId | string | false | ID of the Prediction Environment the challenger should use. |
ChallengersReplaySettings
{
"properties": {
"enabled": {
"description": "Identifies whether scheduled replay is enabled.",
"type": "boolean",
"x-versionadded": "v2.33"
},
"schedule": {
"description": "The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False.",
"properties": {
"dayOfMonth": {
"description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.",
"items": {
"enum": [
"*",
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23,
24,
25,
26,
27,
28,
29,
30,
31
],
"type": [
"number",
"string"
]
},
"maxItems": 31,
"type": "array"
},
"dayOfWeek": {
"description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.",
"items": {
"enum": [
"*",
0,
1,
2,
3,
4,
5,
6,
"sunday",
"SUNDAY",
"Sunday",
"monday",
"MONDAY",
"Monday",
"tuesday",
"TUESDAY",
"Tuesday",
"wednesday",
"WEDNESDAY",
"Wednesday",
"thursday",
"THURSDAY",
"Thursday",
"friday",
"FRIDAY",
"Friday",
"saturday",
"SATURDAY",
"Saturday",
"sun",
"SUN",
"Sun",
"mon",
"MON",
"Mon",
"tue",
"TUE",
"Tue",
"wed",
"WED",
"Wed",
"thu",
"THU",
"Thu",
"fri",
"FRI",
"Fri",
"sat",
"SAT",
"Sat"
],
"type": [
"number",
"string"
]
},
"maxItems": 7,
"type": "array"
},
"hour": {
"description": "The hour(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every hour of the day or ``[0 ... 23]``.",
"items": {
"enum": [
"*",
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23
],
"type": [
"number",
"string"
]
},
"maxItems": 24,
"type": "array"
},
"minute": {
"description": "The minute(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every minute of the day or``[0 ... 59]``.",
"items": {
"enum": [
"*",
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23,
24,
25,
26,
27,
28,
29,
30,
31,
32,
33,
34,
35,
36,
37,
38,
39,
40,
41,
42,
43,
44,
45,
46,
47,
48,
49,
50,
51,
52,
53,
54,
55,
56,
57,
58,
59
],
"type": [
"number",
"string"
]
},
"maxItems": 60,
"type": "array"
},
"month": {
"description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.",
"items": {
"enum": [
"*",
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
"january",
"JANUARY",
"January",
"february",
"FEBRUARY",
"February",
"march",
"MARCH",
"March",
"april",
"APRIL",
"April",
"may",
"MAY",
"May",
"june",
"JUNE",
"June",
"july",
"JULY",
"July",
"august",
"AUGUST",
"August",
"september",
"SEPTEMBER",
"September",
"october",
"OCTOBER",
"October",
"november",
"NOVEMBER",
"November",
"december",
"DECEMBER",
"December",
"jan",
"JAN",
"Jan",
"feb",
"FEB",
"Feb",
"mar",
"MAR",
"Mar",
"apr",
"APR",
"Apr",
"jun",
"JUN",
"Jun",
"jul",
"JUL",
"Jul",
"aug",
"AUG",
"Aug",
"sep",
"SEP",
"Sep",
"oct",
"OCT",
"Oct",
"nov",
"NOV",
"Nov",
"dec",
"DEC",
"Dec"
],
"type": [
"number",
"string"
]
},
"maxItems": 12,
"type": "array"
}
},
"required": [
"dayOfMonth",
"dayOfWeek",
"hour",
"minute",
"month"
],
"type": "object"
}
},
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| enabled | boolean | false | Identifies whether scheduled replay is enabled. | |
| schedule | Schedule | false | The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False. |
CustomModelDetails
{
"description": "Details of the custom model associated to this registered model version",
"properties": {
"createdAt": {
"description": "Time when the custom model was created",
"type": "string"
},
"creatorEmail": {
"description": "Email of the user who created the custom model",
"type": [
"string",
"null"
]
},
"creatorId": {
"description": "ID of the creator of the custom model",
"type": "string"
},
"creatorName": {
"description": "Name of the user who created the custom model",
"type": [
"string",
"null"
]
},
"id": {
"description": "ID of the associated custom model",
"type": "string"
},
"versionLabel": {
"description": "Label of associated custom model version.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.34"
}
},
"required": [
"createdAt",
"creatorId",
"id"
],
"type": "object"
}
Details of the custom model associated to this registered model version
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| createdAt | string | true | Time when the custom model was created | |
| creatorEmail | string,null | false | Email of the user who created the custom model | |
| creatorId | string | true | ID of the creator of the custom model | |
| creatorName | string,null | false | Name of the user who created the custom model | |
| id | string | true | ID of the associated custom model | |
| versionLabel | string,null | false | Label of associated custom model version. |
MlpkgFileContents
{
"description": "Information about the content of .mlpkg artifact",
"properties": {
"allTimeSeriesPredictionIntervals": {
"description": "Whether .mlpkg contains TS prediction intervals computed for all percentiles",
"type": [
"boolean",
"null"
],
"x-versionadded": "v2.31"
}
},
"type": "object"
}
Information about the content of .mlpkg artifact
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| allTimeSeriesPredictionIntervals | boolean,null | false | Whether .mlpkg contains TS prediction intervals computed for all percentiles |
ModelPackageCapabilities
{
"description": "Capabilities of the current model package.",
"properties": {
"supportsAutomaticActuals": {
"description": "Whether inferring actual values from time series history data and automatically feeding them back for accuracy estimation is supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2",
"x-versiondeprecated": "v2.29"
},
"supportsChallengerModels": {
"description": "Whether Challenger Models are supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2",
"x-versiondeprecated": "v2.29"
},
"supportsFeatureDriftTracking": {
"description": "Whether Feature Drift is supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2",
"x-versiondeprecated": "v2.29"
},
"supportsHumilityRecommendedRules": {
"description": "Whether calculating values for recommended Humility Rules is supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2",
"x-versiondeprecated": "v2.29"
},
"supportsHumilityRules": {
"description": "Whether Humility Rules are supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2",
"x-versiondeprecated": "v2.29"
},
"supportsHumilityRulesDefaultCalculations": {
"description": "Whether calculating default values for Humility Rules is supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2"
},
"supportsPredictionWarning": {
"description": "Whether Prediction Warnings are supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2",
"x-versiondeprecated": "v2.29"
},
"supportsRetraining": {
"description": "Whether deployment supports retraining.",
"type": "boolean",
"x-versionadded": "v2.28",
"x-versiondeprecated": "v2.29"
},
"supportsScoringCodeDownload": {
"description": "Whether scoring code download is supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2",
"x-versiondeprecated": "v2.29"
},
"supportsSecondaryDatasets": {
"description": "If the deployments supports secondary datasets.",
"type": "boolean",
"x-versionadded": "v2.28",
"x-versiondeprecated": "v2.29"
},
"supportsSegmentedAnalysisDriftAndAccuracy": {
"description": "Whether tracking features in training and predictions data for segmented analysis is supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2",
"x-versiondeprecated": "v2.29"
},
"supportsShapBasedPredictionExplanations": {
"description": "Whether shap-based prediction explanations are supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2",
"x-versiondeprecated": "v2.29"
},
"supportsTargetDriftTracking": {
"description": "Whether Target Drift is supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2",
"x-versiondeprecated": "v2.29"
}
},
"required": [
"supportsChallengerModels",
"supportsFeatureDriftTracking",
"supportsHumilityRecommendedRules",
"supportsHumilityRules",
"supportsHumilityRulesDefaultCalculations",
"supportsPredictionWarning",
"supportsSecondaryDatasets",
"supportsSegmentedAnalysisDriftAndAccuracy",
"supportsShapBasedPredictionExplanations",
"supportsTargetDriftTracking"
],
"type": "object"
}
Capabilities of the current model package.
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| supportsAutomaticActuals | boolean | false | Whether inferring actual values from time series history data and automatically feeding them back for accuracy estimation is supported by this model package. | |
| supportsChallengerModels | boolean | true | Whether Challenger Models are supported by this model package. | |
| supportsFeatureDriftTracking | boolean | true | Whether Feature Drift is supported by this model package. | |
| supportsHumilityRecommendedRules | boolean | true | Whether calculating values for recommended Humility Rules is supported by this model package. | |
| supportsHumilityRules | boolean | true | Whether Humility Rules are supported by this model package. | |
| supportsHumilityRulesDefaultCalculations | boolean | true | Whether calculating default values for Humility Rules is supported by this model package. | |
| supportsPredictionWarning | boolean | true | Whether Prediction Warnings are supported by this model package. | |
| supportsRetraining | boolean | false | Whether deployment supports retraining. | |
| supportsScoringCodeDownload | boolean | false | Whether scoring code download is supported by this model package. | |
| supportsSecondaryDatasets | boolean | true | If the deployments supports secondary datasets. | |
| supportsSegmentedAnalysisDriftAndAccuracy | boolean | true | Whether tracking features in training and predictions data for segmented analysis is supported by this model package. | |
| supportsShapBasedPredictionExplanations | boolean | true | Whether shap-based prediction explanations are supported by this model package. | |
| supportsTargetDriftTracking | boolean | true | Whether Target Drift is supported by this model package. |
ModelPackageDatasets
{
"description": "dataset information for the model package",
"properties": {
"baselineSegmentedBy": {
"description": "Names of categorical features by which the training baseline was segmented. This allows for deployment prediction requests to be segmented by those same features. Segmenting the training baseline by these features allows for users to perform segmented analysis of Data Drift and Accuracy, and to compare the same subset of training and scoring data based on the selected segment attribute and segment value.",
"items": {
"type": "string"
},
"type": "array"
},
"datasetName": {
"description": "Name of dataset used to train the model",
"type": [
"string",
"null"
]
},
"holdoutDataCatalogId": {
"description": "ID for holdout data (returned from uploading a data set)",
"type": [
"string",
"null"
]
},
"holdoutDataCatalogVersionId": {
"description": "Version ID for holdout data (returned from uploading a data set)",
"type": [
"string",
"null"
]
},
"holdoutDataCreatedAt": {
"description": "Time when the holdout data item was created",
"type": [
"string",
"null"
],
"x-versionadded": "v2.34"
},
"holdoutDataCreatorEmail": {
"description": "Email of the user who created the holdout data item",
"type": [
"string",
"null"
],
"x-versionadded": "v2.34"
},
"holdoutDataCreatorId": {
"default": null,
"description": "ID of the creator of the holdout data item",
"type": [
"string",
"null"
],
"x-versionadded": "v2.34"
},
"holdoutDataCreatorName": {
"description": "Name of the user who created the holdout data item",
"type": [
"string",
"null"
],
"x-versionadded": "v2.34"
},
"holdoutDatasetName": {
"description": "Name of dataset used for model holdout",
"type": [
"string",
"null"
]
},
"targetHistogramBaseline": {
"description": "Values used to establish the training baseline",
"enum": [
"predictions",
"actuals"
],
"type": "string"
},
"trainingDataCatalogId": {
"description": "ID for training data (returned from uploading a data set)",
"type": [
"string",
"null"
]
},
"trainingDataCatalogVersionId": {
"description": "Version ID for training data (returned from uploading a data set)",
"type": [
"string",
"null"
]
},
"trainingDataCreatedAt": {
"description": "Time when the training data item was created",
"type": [
"string",
"null"
],
"x-versionadded": "v2.34"
},
"trainingDataCreatorEmail": {
"description": "Email of the user who created the training data item",
"type": [
"string",
"null"
],
"x-versionadded": "v2.34"
},
"trainingDataCreatorId": {
"default": null,
"description": "ID of the creator of the training data item",
"type": [
"string",
"null"
],
"x-versionadded": "v2.34"
},
"trainingDataCreatorName": {
"description": "Name of the user who created the training data item",
"type": [
"string",
"null"
],
"x-versionadded": "v2.34"
},
"trainingDataSize": {
"description": "Number of rows in training data (used by DR models)",
"type": "integer"
}
},
"required": [
"baselineSegmentedBy",
"datasetName",
"holdoutDataCatalogId",
"holdoutDataCatalogVersionId",
"holdoutDatasetName",
"trainingDataCatalogId",
"trainingDataCatalogVersionId"
],
"type": "object"
}
dataset information for the model package
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| baselineSegmentedBy | [string] | true | Names of categorical features by which the training baseline was segmented. This allows for deployment prediction requests to be segmented by those same features. Segmenting the training baseline by these features allows for users to perform segmented analysis of Data Drift and Accuracy, and to compare the same subset of training and scoring data based on the selected segment attribute and segment value. | |
| datasetName | string,null | true | Name of dataset used to train the model | |
| holdoutDataCatalogId | string,null | true | ID for holdout data (returned from uploading a data set) | |
| holdoutDataCatalogVersionId | string,null | true | Version ID for holdout data (returned from uploading a data set) | |
| holdoutDataCreatedAt | string,null | false | Time when the holdout data item was created | |
| holdoutDataCreatorEmail | string,null | false | Email of the user who created the holdout data item | |
| holdoutDataCreatorId | string,null | false | ID of the creator of the holdout data item | |
| holdoutDataCreatorName | string,null | false | Name of the user who created the holdout data item | |
| holdoutDatasetName | string,null | true | Name of dataset used for model holdout | |
| targetHistogramBaseline | string | false | Values used to establish the training baseline | |
| trainingDataCatalogId | string,null | true | ID for training data (returned from uploading a data set) | |
| trainingDataCatalogVersionId | string,null | true | Version ID for training data (returned from uploading a data set) | |
| trainingDataCreatedAt | string,null | false | Time when the training data item was created | |
| trainingDataCreatorEmail | string,null | false | Email of the user who created the training data item | |
| trainingDataCreatorId | string,null | false | ID of the creator of the training data item | |
| trainingDataCreatorName | string,null | false | Name of the user who created the training data item | |
| trainingDataSize | integer | false | Number of rows in training data (used by DR models) |
Enumerated Values¶
| Property | Value |
|---|---|
| targetHistogramBaseline | [predictions, actuals] |
ModelPackageImportMeta
{
"description": "Information from when this Model Package was first saved",
"properties": {
"containsFearPipeline": {
"description": "Exists for imported models only, indicates thatmodel package contains file with fear pipeline.",
"type": [
"boolean",
"null"
]
},
"containsFeaturelists": {
"description": "Exists for imported models only, indicates thatmodel package contains file with featurelists.",
"type": [
"boolean",
"null"
]
},
"containsLeaderboardMeta": {
"description": "Exists for imported models only, indicates thatmodel package contains file with leaderboard meta.",
"type": [
"boolean",
"null"
]
},
"containsProjectMeta": {
"description": "Exists for imported models only, indicates thatmodel package contains file with project meta.",
"type": [
"boolean",
"null"
]
},
"creatorFullName": {
"description": "Full name of the person who created this model package",
"type": [
"string",
"null"
]
},
"creatorId": {
"description": "User ID of the person who created this Model Package",
"type": "string"
},
"creatorUsername": {
"description": "Username of the person who created this model package",
"type": "string"
},
"dateCreated": {
"description": "When this Model Package was created",
"type": "string"
},
"originalFileName": {
"description": "Exists for imported models only, the original file name that was uploaded",
"type": [
"string",
"null"
]
}
},
"required": [
"creatorFullName",
"creatorId",
"creatorUsername",
"dateCreated",
"originalFileName"
],
"type": "object"
}
Information from when this Model Package was first saved
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| containsFearPipeline | boolean,null | false | Exists for imported models only, indicates thatmodel package contains file with fear pipeline. | |
| containsFeaturelists | boolean,null | false | Exists for imported models only, indicates thatmodel package contains file with featurelists. | |
| containsLeaderboardMeta | boolean,null | false | Exists for imported models only, indicates thatmodel package contains file with leaderboard meta. | |
| containsProjectMeta | boolean,null | false | Exists for imported models only, indicates thatmodel package contains file with project meta. | |
| creatorFullName | string,null | true | Full name of the person who created this model package | |
| creatorId | string | true | User ID of the person who created this Model Package | |
| creatorUsername | string | true | Username of the person who created this model package | |
| dateCreated | string | true | When this Model Package was created | |
| originalFileName | string,null | true | Exists for imported models only, the original file name that was uploaded |
ModelPackageModelDescription
{
"description": "model description information for the model package",
"properties": {
"buildEnvironmentType": {
"description": "build environment type of the model",
"enum": [
"DataRobot",
"Python",
"R",
"Java",
"Other"
],
"type": "string"
},
"description": {
"description": "a description of the model",
"type": [
"string",
"null"
]
},
"location": {
"description": "location of the model",
"type": [
"string",
"null"
]
},
"modelCreatedAt": {
"description": "time when the model was created",
"type": [
"string",
"null"
]
},
"modelCreatorEmail": {
"description": "email of the user who created the model",
"type": [
"string",
"null"
]
},
"modelCreatorId": {
"default": null,
"description": "ID of the creator of the model",
"type": [
"string",
"null"
]
},
"modelCreatorName": {
"description": "name of the user who created the model",
"type": [
"string",
"null"
]
},
"modelName": {
"description": "model name",
"type": "string"
}
},
"required": [
"buildEnvironmentType",
"description",
"location"
],
"type": "object"
}
model description information for the model package
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| buildEnvironmentType | string | true | build environment type of the model | |
| description | string,null | true | a description of the model | |
| location | string,null | true | location of the model | |
| modelCreatedAt | string,null | false | time when the model was created | |
| modelCreatorEmail | string,null | false | email of the user who created the model | |
| modelCreatorId | string,null | false | ID of the creator of the model | |
| modelCreatorName | string,null | false | name of the user who created the model | |
| modelName | string | false | model name |
Enumerated Values¶
| Property | Value |
|---|---|
| buildEnvironmentType | [DataRobot, Python, R, Java, Other] |
ModelPackageModelKind
{
"description": "Model attribute information",
"properties": {
"isAnomalyDetectionModel": {
"description": "true if this is an anomaly detection model",
"type": "boolean"
},
"isCombinedModel": {
"description": "true if model is a combined model",
"type": "boolean",
"x-versionadded": "v2.27"
},
"isFeatureDiscovery": {
"description": "true if this model uses the Feature Discovery feature",
"type": "boolean"
},
"isMultiseries": {
"description": "true if model is multiseries",
"type": "boolean"
},
"isTimeSeries": {
"description": "true if model is time series",
"type": "boolean"
},
"isUnsupervisedLearning": {
"description": "true if model used unsupervised learning",
"type": "boolean"
}
},
"required": [
"isAnomalyDetectionModel",
"isCombinedModel",
"isFeatureDiscovery",
"isMultiseries",
"isTimeSeries",
"isUnsupervisedLearning"
],
"type": "object"
}
Model attribute information
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| isAnomalyDetectionModel | boolean | true | true if this is an anomaly detection model | |
| isCombinedModel | boolean | true | true if model is a combined model | |
| isFeatureDiscovery | boolean | true | true if this model uses the Feature Discovery feature | |
| isMultiseries | boolean | true | true if model is multiseries | |
| isTimeSeries | boolean | true | true if model is time series | |
| isUnsupervisedLearning | boolean | true | true if model used unsupervised learning |
ModelPackageResponse
{
"description": "modelPackage of the challenger.",
"properties": {
"id": {
"description": "ID of the model package.",
"type": "string"
},
"name": {
"description": "Type of the current model.",
"type": "string"
},
"registeredModelId": {
"description": "ID of the associated registered model",
"type": [
"string",
"null"
]
}
},
"required": [
"id",
"name"
],
"type": "object"
}
modelPackage of the challenger.
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| id | string | true | ID of the model package. | |
| name | string | true | Type of the current model. | |
| registeredModelId | string,null | false | ID of the associated registered model |
ModelPackageRetrieveResponseBase
{
"properties": {
"buildStatus": {
"description": "Model package build status",
"enum": [
"inProgress",
"complete",
"failed"
],
"type": [
"string",
"null"
],
"x-versionadded": "v2.33"
},
"capabilities": {
"description": "Capabilities of the current model package.",
"properties": {
"supportsAutomaticActuals": {
"description": "Whether inferring actual values from time series history data and automatically feeding them back for accuracy estimation is supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2",
"x-versiondeprecated": "v2.29"
},
"supportsChallengerModels": {
"description": "Whether Challenger Models are supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2",
"x-versiondeprecated": "v2.29"
},
"supportsFeatureDriftTracking": {
"description": "Whether Feature Drift is supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2",
"x-versiondeprecated": "v2.29"
},
"supportsHumilityRecommendedRules": {
"description": "Whether calculating values for recommended Humility Rules is supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2",
"x-versiondeprecated": "v2.29"
},
"supportsHumilityRules": {
"description": "Whether Humility Rules are supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2",
"x-versiondeprecated": "v2.29"
},
"supportsHumilityRulesDefaultCalculations": {
"description": "Whether calculating default values for Humility Rules is supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2"
},
"supportsPredictionWarning": {
"description": "Whether Prediction Warnings are supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2",
"x-versiondeprecated": "v2.29"
},
"supportsRetraining": {
"description": "Whether deployment supports retraining.",
"type": "boolean",
"x-versionadded": "v2.28",
"x-versiondeprecated": "v2.29"
},
"supportsScoringCodeDownload": {
"description": "Whether scoring code download is supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2",
"x-versiondeprecated": "v2.29"
},
"supportsSecondaryDatasets": {
"description": "If the deployments supports secondary datasets.",
"type": "boolean",
"x-versionadded": "v2.28",
"x-versiondeprecated": "v2.29"
},
"supportsSegmentedAnalysisDriftAndAccuracy": {
"description": "Whether tracking features in training and predictions data for segmented analysis is supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2",
"x-versiondeprecated": "v2.29"
},
"supportsShapBasedPredictionExplanations": {
"description": "Whether shap-based prediction explanations are supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2",
"x-versiondeprecated": "v2.29"
},
"supportsTargetDriftTracking": {
"description": "Whether Target Drift is supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2",
"x-versiondeprecated": "v2.29"
}
},
"required": [
"supportsChallengerModels",
"supportsFeatureDriftTracking",
"supportsHumilityRecommendedRules",
"supportsHumilityRules",
"supportsHumilityRulesDefaultCalculations",
"supportsPredictionWarning",
"supportsSecondaryDatasets",
"supportsSegmentedAnalysisDriftAndAccuracy",
"supportsShapBasedPredictionExplanations",
"supportsTargetDriftTracking"
],
"type": "object"
},
"datasets": {
"description": "dataset information for the model package",
"properties": {
"baselineSegmentedBy": {
"description": "Names of categorical features by which the training baseline was segmented. This allows for deployment prediction requests to be segmented by those same features. Segmenting the training baseline by these features allows for users to perform segmented analysis of Data Drift and Accuracy, and to compare the same subset of training and scoring data based on the selected segment attribute and segment value.",
"items": {
"type": "string"
},
"type": "array"
},
"datasetName": {
"description": "Name of dataset used to train the model",
"type": [
"string",
"null"
]
},
"holdoutDataCatalogId": {
"description": "ID for holdout data (returned from uploading a data set)",
"type": [
"string",
"null"
]
},
"holdoutDataCatalogVersionId": {
"description": "Version ID for holdout data (returned from uploading a data set)",
"type": [
"string",
"null"
]
},
"holdoutDataCreatedAt": {
"description": "Time when the holdout data item was created",
"type": [
"string",
"null"
],
"x-versionadded": "v2.34"
},
"holdoutDataCreatorEmail": {
"description": "Email of the user who created the holdout data item",
"type": [
"string",
"null"
],
"x-versionadded": "v2.34"
},
"holdoutDataCreatorId": {
"default": null,
"description": "ID of the creator of the holdout data item",
"type": [
"string",
"null"
],
"x-versionadded": "v2.34"
},
"holdoutDataCreatorName": {
"description": "Name of the user who created the holdout data item",
"type": [
"string",
"null"
],
"x-versionadded": "v2.34"
},
"holdoutDatasetName": {
"description": "Name of dataset used for model holdout",
"type": [
"string",
"null"
]
},
"targetHistogramBaseline": {
"description": "Values used to establish the training baseline",
"enum": [
"predictions",
"actuals"
],
"type": "string"
},
"trainingDataCatalogId": {
"description": "ID for training data (returned from uploading a data set)",
"type": [
"string",
"null"
]
},
"trainingDataCatalogVersionId": {
"description": "Version ID for training data (returned from uploading a data set)",
"type": [
"string",
"null"
]
},
"trainingDataCreatedAt": {
"description": "Time when the training data item was created",
"type": [
"string",
"null"
],
"x-versionadded": "v2.34"
},
"trainingDataCreatorEmail": {
"description": "Email of the user who created the training data item",
"type": [
"string",
"null"
],
"x-versionadded": "v2.34"
},
"trainingDataCreatorId": {
"default": null,
"description": "ID of the creator of the training data item",
"type": [
"string",
"null"
],
"x-versionadded": "v2.34"
},
"trainingDataCreatorName": {
"description": "Name of the user who created the training data item",
"type": [
"string",
"null"
],
"x-versionadded": "v2.34"
},
"trainingDataSize": {
"description": "Number of rows in training data (used by DR models)",
"type": "integer"
}
},
"required": [
"baselineSegmentedBy",
"datasetName",
"holdoutDataCatalogId",
"holdoutDataCatalogVersionId",
"holdoutDatasetName",
"trainingDataCatalogId",
"trainingDataCatalogVersionId"
],
"type": "object"
},
"id": {
"description": "ID of the Model package",
"type": "string",
"x-versionadded": "v2.33"
},
"importMeta": {
"description": "Information from when this Model Package was first saved",
"properties": {
"containsFearPipeline": {
"description": "Exists for imported models only, indicates thatmodel package contains file with fear pipeline.",
"type": [
"boolean",
"null"
]
},
"containsFeaturelists": {
"description": "Exists for imported models only, indicates thatmodel package contains file with featurelists.",
"type": [
"boolean",
"null"
]
},
"containsLeaderboardMeta": {
"description": "Exists for imported models only, indicates thatmodel package contains file with leaderboard meta.",
"type": [
"boolean",
"null"
]
},
"containsProjectMeta": {
"description": "Exists for imported models only, indicates thatmodel package contains file with project meta.",
"type": [
"boolean",
"null"
]
},
"creatorFullName": {
"description": "Full name of the person who created this model package",
"type": [
"string",
"null"
]
},
"creatorId": {
"description": "User ID of the person who created this Model Package",
"type": "string"
},
"creatorUsername": {
"description": "Username of the person who created this model package",
"type": "string"
},
"dateCreated": {
"description": "When this Model Package was created",
"type": "string"
},
"originalFileName": {
"description": "Exists for imported models only, the original file name that was uploaded",
"type": [
"string",
"null"
]
}
},
"required": [
"creatorFullName",
"creatorId",
"creatorUsername",
"dateCreated",
"originalFileName"
],
"type": "object"
},
"isArchived": {
"description": "Whether the model package is permanently archived (cannot be used in deployment or replacement)",
"type": "boolean",
"x-versionadded": "v2.33"
},
"isDeprecated": {
"description": "Whether the model package is deprecated. eg. python2 models are deprecated.",
"type": "boolean",
"x-versionadded": "v2.29"
},
"mlpkgFileContents": {
"description": "Information about the content of .mlpkg artifact",
"properties": {
"allTimeSeriesPredictionIntervals": {
"description": "Whether .mlpkg contains TS prediction intervals computed for all percentiles",
"type": [
"boolean",
"null"
],
"x-versionadded": "v2.31"
}
},
"type": "object"
},
"modelDescription": {
"description": "model description information for the model package",
"properties": {
"buildEnvironmentType": {
"description": "build environment type of the model",
"enum": [
"DataRobot",
"Python",
"R",
"Java",
"Other"
],
"type": "string"
},
"description": {
"description": "a description of the model",
"type": [
"string",
"null"
]
},
"location": {
"description": "location of the model",
"type": [
"string",
"null"
]
},
"modelCreatedAt": {
"description": "time when the model was created",
"type": [
"string",
"null"
]
},
"modelCreatorEmail": {
"description": "email of the user who created the model",
"type": [
"string",
"null"
]
},
"modelCreatorId": {
"default": null,
"description": "ID of the creator of the model",
"type": [
"string",
"null"
]
},
"modelCreatorName": {
"description": "name of the user who created the model",
"type": [
"string",
"null"
]
},
"modelName": {
"description": "model name",
"type": "string"
}
},
"required": [
"buildEnvironmentType",
"description",
"location"
],
"type": "object"
},
"modelExecutionType": {
"description": "Type of model package. `dedicated` (native DataRobot models) and `custom_inference_model` (user added inference models) both execute on DataRobot prediction servers, `external` do not",
"enum": [
"dedicated",
"custom_inference_model",
"external"
],
"type": "string",
"x-versionadded": "v2.33"
},
"modelId": {
"description": "ID of the model",
"type": "string",
"x-versionadded": "v2.33"
},
"modelKind": {
"description": "Model attribute information",
"properties": {
"isAnomalyDetectionModel": {
"description": "true if this is an anomaly detection model",
"type": "boolean"
},
"isCombinedModel": {
"description": "true if model is a combined model",
"type": "boolean",
"x-versionadded": "v2.27"
},
"isFeatureDiscovery": {
"description": "true if this model uses the Feature Discovery feature",
"type": "boolean"
},
"isMultiseries": {
"description": "true if model is multiseries",
"type": "boolean"
},
"isTimeSeries": {
"description": "true if model is time series",
"type": "boolean"
},
"isUnsupervisedLearning": {
"description": "true if model used unsupervised learning",
"type": "boolean"
}
},
"required": [
"isAnomalyDetectionModel",
"isCombinedModel",
"isFeatureDiscovery",
"isMultiseries",
"isTimeSeries",
"isUnsupervisedLearning"
],
"type": "object"
},
"name": {
"description": "Model package name",
"type": "string",
"x-versionadded": "v2.33"
},
"sourceMeta": {
"description": "Meta information from where this model was generated",
"properties": {
"customModelDetails": {
"description": "Details of the custom model associated to this registered model version",
"properties": {
"createdAt": {
"description": "Time when the custom model was created",
"type": "string"
},
"creatorEmail": {
"description": "Email of the user who created the custom model",
"type": [
"string",
"null"
]
},
"creatorId": {
"description": "ID of the creator of the custom model",
"type": "string"
},
"creatorName": {
"description": "Name of the user who created the custom model",
"type": [
"string",
"null"
]
},
"id": {
"description": "ID of the associated custom model",
"type": "string"
},
"versionLabel": {
"description": "Label of associated custom model version.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.34"
}
},
"required": [
"createdAt",
"creatorId",
"id"
],
"type": "object"
},
"environmentUrl": {
"description": "If available, URL of the source model",
"format": "uri",
"type": [
"string",
"null"
]
},
"fips_140_2Enabled": {
"description": "true if the model was built with FIPS-140-2",
"type": "boolean"
},
"projectCreatedAt": {
"description": "If available, time when the project was created",
"type": [
"string",
"null"
]
},
"projectCreatorEmail": {
"description": "If available, email of the user who created the project",
"type": [
"string",
"null"
]
},
"projectCreatorId": {
"default": null,
"description": "If available, ID of the creator of the project",
"type": [
"string",
"null"
]
},
"projectCreatorName": {
"description": "If available, name of the user who created the project",
"type": [
"string",
"null"
]
},
"projectId": {
"description": "If available, the project id used for this model",
"type": [
"string",
"null"
]
},
"projectName": {
"description": "If available, the project name for this model",
"type": [
"string",
"null"
]
},
"scoringCode": {
"description": "If available, information about the model's scoring code",
"properties": {
"dataRobotPredictionVersion": {
"description": "DataRobot prediction API version for the scoring code",
"type": [
"string",
"null"
]
},
"location": {
"description": "Location of the scoring code",
"enum": [
"local_leaderboard",
"mlpkg"
],
"type": [
"string",
"null"
]
}
},
"required": [
"dataRobotPredictionVersion",
"location"
],
"type": "object"
},
"useCaseDetails": {
"description": "Details of the use-case associated to this registered model version",
"properties": {
"createdAt": {
"description": "Time when use-case was created",
"type": "string"
},
"creatorEmail": {
"description": "Email of the user who created use-case",
"type": [
"string",
"null"
]
},
"creatorId": {
"description": "ID of the creator of the use-case",
"type": "string"
},
"creatorName": {
"description": "Name of the user who created use-case",
"type": [
"string",
"null"
]
},
"id": {
"description": "ID of the associated use-case",
"type": "string"
},
"name": {
"description": "Name of the use case at the moment of creation",
"type": [
"string",
"null"
]
}
},
"required": [
"createdAt",
"creatorId",
"id"
],
"type": "object"
}
},
"required": [
"environmentUrl",
"projectId",
"projectName",
"scoringCode"
],
"type": "object"
},
"target": {
"description": "target information for the model package",
"properties": {
"classCount": {
"description": "Number of classes for classification models.",
"minimum": 0,
"type": [
"integer",
"null"
]
},
"classNames": {
"description": "Class names for prediction results. When target type is Binary, two class names are returned. The first element is the minority (positive) class and the second element is the majority (negative) class. Limited to 100 returned for Multiclass.",
"items": {
"type": "string"
},
"maxItems": 100,
"type": "array"
},
"name": {
"description": "name of the target column",
"type": "string"
},
"predictionProbabilitiesColumn": {
"description": "Field or column name containing prediction probabilities",
"type": [
"string",
"null"
]
},
"predictionThreshold": {
"description": "Prediction threshold used for binary classification models",
"maximum": 1,
"minimum": 0,
"type": [
"number",
"null"
]
},
"type": {
"description": "Target type of the model.",
"enum": [
"Binary",
"Regression",
"Multiclass",
"Multilabel",
"TextGeneration",
"GeoPoint",
"AgenticWorkflow"
],
"type": "string"
}
},
"required": [
"classCount",
"classNames",
"name",
"predictionProbabilitiesColumn",
"predictionThreshold",
"type"
],
"type": "object"
},
"timeseries": {
"description": "time series information for the model package",
"properties": {
"datetimeColumnFormat": {
"description": "Date format for forecast date and forecast point column",
"type": [
"string",
"null"
]
},
"datetimeColumnName": {
"description": "Name of the forecast date column",
"type": [
"string",
"null"
]
},
"effectiveFeatureDerivationWindowEnd": {
"description": "Same concept as `featureDerivationWindowEnd` which is chosen by the user and based on the initial sampled data from the eda sample. When the dataset goes through aim, the pipeline reads the full dataset and figures out the \"real\" FDW (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW.",
"maximum": 0,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.25"
},
"effectiveFeatureDerivationWindowStart": {
"description": "Same concept as `featureDerivationWindowStart` which is chosen by the user and based on the initial sampled data from the eda sample. When the dataset goes through aim, the pipeline reads the full dataset and figures out the \"real\" FDW (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW.",
"maximum": 0,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.25"
},
"featureDerivationWindowEnd": {
"description": "Negative number or zero defining how many time units of the forecast distances time unit into the past relative to the forecast point the feature derivation window should end.",
"maximum": 0,
"type": [
"integer",
"null"
]
},
"featureDerivationWindowStart": {
"description": "Negative number or zero defining how many time units of the forecast distances time unit into the past relative to the forecast point the feature derivation window should begin.",
"maximum": 0,
"type": [
"integer",
"null"
]
},
"forecastDistanceColumnName": {
"description": "Name of the forecast distance column",
"type": [
"string",
"null"
]
},
"forecastDistances": {
"description": "List of integer forecast distances",
"items": {
"type": "integer"
},
"type": "array"
},
"forecastDistancesTimeUnit": {
"description": "The time unit of forecast distances",
"enum": [
"MICROSECOND",
"MILLISECOND",
"SECOND",
"MINUTE",
"HOUR",
"DAY",
"WEEK",
"MONTH",
"QUARTER",
"YEAR"
],
"type": [
"string",
"null"
]
},
"forecastPointColumnName": {
"description": "Name of the forecast point column",
"type": [
"string",
"null"
]
},
"isCrossSeries": {
"description": "true if the model is cross-series.",
"type": [
"boolean",
"null"
]
},
"isNewSeriesSupport": {
"description": "true if the model is optimized to support new series.",
"type": [
"boolean",
"null"
],
"x-versionadded": "v2.25"
},
"isTraditionalTimeSeries": {
"description": "true if the model is traditional time series.",
"type": [
"boolean",
"null"
]
},
"seriesColumnName": {
"description": "Name of the series column in case of multi-series date",
"type": [
"string",
"null"
]
}
},
"required": [
"datetimeColumnFormat",
"datetimeColumnName",
"effectiveFeatureDerivationWindowEnd",
"effectiveFeatureDerivationWindowStart",
"featureDerivationWindowEnd",
"featureDerivationWindowStart",
"forecastDistanceColumnName",
"forecastDistances",
"forecastDistancesTimeUnit",
"forecastPointColumnName",
"isCrossSeries",
"isNewSeriesSupport",
"isTraditionalTimeSeries",
"seriesColumnName"
],
"type": "object"
},
"updatedBy": {
"description": "Information on the user who last modified the registered model",
"properties": {
"email": {
"description": "Email of the user",
"type": [
"string",
"null"
]
},
"id": {
"description": "ID of the user",
"type": "string"
},
"name": {
"description": "Full name of the user",
"type": [
"string",
"null"
]
}
},
"required": [
"email",
"id",
"name"
],
"type": "object"
},
"userProvidedId": {
"description": "A user-provided unique ID associated with the given custom inference model.",
"type": "string",
"x-versionadded": "v2.33"
}
},
"required": [
"capabilities",
"datasets",
"id",
"importMeta",
"isArchived",
"isDeprecated",
"modelDescription",
"modelExecutionType",
"modelId",
"modelKind",
"name",
"sourceMeta",
"target",
"timeseries",
"updatedBy"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| buildStatus | string,null | false | Model package build status | |
| capabilities | ModelPackageCapabilities | true | Capabilities of the current model package. | |
| datasets | ModelPackageDatasets | true | dataset information for the model package | |
| id | string | true | ID of the Model package | |
| importMeta | ModelPackageImportMeta | true | Information from when this Model Package was first saved | |
| isArchived | boolean | true | Whether the model package is permanently archived (cannot be used in deployment or replacement) | |
| isDeprecated | boolean | true | Whether the model package is deprecated. eg. python2 models are deprecated. | |
| mlpkgFileContents | MlpkgFileContents | false | Information about the content of .mlpkg artifact | |
| modelDescription | ModelPackageModelDescription | true | model description information for the model package | |
| modelExecutionType | string | true | Type of model package. dedicated (native DataRobot models) and custom_inference_model (user added inference models) both execute on DataRobot prediction servers, external do not |
|
| modelId | string | true | ID of the model | |
| modelKind | ModelPackageModelKind | true | Model attribute information | |
| name | string | true | Model package name | |
| sourceMeta | ModelPackageSourceMeta | true | Meta information from where this model was generated | |
| target | ModelPackageTarget | true | target information for the model package | |
| timeseries | ModelPackageTimeseries | true | time series information for the model package | |
| updatedBy | UserMetadata | true | Information on the user who last modified the registered model | |
| userProvidedId | string | false | A user-provided unique ID associated with the given custom inference model. |
Enumerated Values¶
| Property | Value |
|---|---|
| buildStatus | [inProgress, complete, failed] |
| modelExecutionType | [dedicated, custom_inference_model, external] |
ModelPackageScoringCodeMeta
{
"description": "If available, information about the model's scoring code",
"properties": {
"dataRobotPredictionVersion": {
"description": "DataRobot prediction API version for the scoring code",
"type": [
"string",
"null"
]
},
"location": {
"description": "Location of the scoring code",
"enum": [
"local_leaderboard",
"mlpkg"
],
"type": [
"string",
"null"
]
}
},
"required": [
"dataRobotPredictionVersion",
"location"
],
"type": "object"
}
If available, information about the model's scoring code
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| dataRobotPredictionVersion | string,null | true | DataRobot prediction API version for the scoring code | |
| location | string,null | true | Location of the scoring code |
Enumerated Values¶
| Property | Value |
|---|---|
| location | [local_leaderboard, mlpkg] |
ModelPackageSourceMeta
{
"description": "Meta information from where this model was generated",
"properties": {
"customModelDetails": {
"description": "Details of the custom model associated to this registered model version",
"properties": {
"createdAt": {
"description": "Time when the custom model was created",
"type": "string"
},
"creatorEmail": {
"description": "Email of the user who created the custom model",
"type": [
"string",
"null"
]
},
"creatorId": {
"description": "ID of the creator of the custom model",
"type": "string"
},
"creatorName": {
"description": "Name of the user who created the custom model",
"type": [
"string",
"null"
]
},
"id": {
"description": "ID of the associated custom model",
"type": "string"
},
"versionLabel": {
"description": "Label of associated custom model version.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.34"
}
},
"required": [
"createdAt",
"creatorId",
"id"
],
"type": "object"
},
"environmentUrl": {
"description": "If available, URL of the source model",
"format": "uri",
"type": [
"string",
"null"
]
},
"fips_140_2Enabled": {
"description": "true if the model was built with FIPS-140-2",
"type": "boolean"
},
"projectCreatedAt": {
"description": "If available, time when the project was created",
"type": [
"string",
"null"
]
},
"projectCreatorEmail": {
"description": "If available, email of the user who created the project",
"type": [
"string",
"null"
]
},
"projectCreatorId": {
"default": null,
"description": "If available, ID of the creator of the project",
"type": [
"string",
"null"
]
},
"projectCreatorName": {
"description": "If available, name of the user who created the project",
"type": [
"string",
"null"
]
},
"projectId": {
"description": "If available, the project id used for this model",
"type": [
"string",
"null"
]
},
"projectName": {
"description": "If available, the project name for this model",
"type": [
"string",
"null"
]
},
"scoringCode": {
"description": "If available, information about the model's scoring code",
"properties": {
"dataRobotPredictionVersion": {
"description": "DataRobot prediction API version for the scoring code",
"type": [
"string",
"null"
]
},
"location": {
"description": "Location of the scoring code",
"enum": [
"local_leaderboard",
"mlpkg"
],
"type": [
"string",
"null"
]
}
},
"required": [
"dataRobotPredictionVersion",
"location"
],
"type": "object"
},
"useCaseDetails": {
"description": "Details of the use-case associated to this registered model version",
"properties": {
"createdAt": {
"description": "Time when use-case was created",
"type": "string"
},
"creatorEmail": {
"description": "Email of the user who created use-case",
"type": [
"string",
"null"
]
},
"creatorId": {
"description": "ID of the creator of the use-case",
"type": "string"
},
"creatorName": {
"description": "Name of the user who created use-case",
"type": [
"string",
"null"
]
},
"id": {
"description": "ID of the associated use-case",
"type": "string"
},
"name": {
"description": "Name of the use case at the moment of creation",
"type": [
"string",
"null"
]
}
},
"required": [
"createdAt",
"creatorId",
"id"
],
"type": "object"
}
},
"required": [
"environmentUrl",
"projectId",
"projectName",
"scoringCode"
],
"type": "object"
}
Meta information from where this model was generated
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| customModelDetails | CustomModelDetails | false | Details of the custom model associated to this registered model version | |
| environmentUrl | string,null(uri) | true | If available, URL of the source model | |
| fips_140_2Enabled | boolean | false | true if the model was built with FIPS-140-2 | |
| projectCreatedAt | string,null | false | If available, time when the project was created | |
| projectCreatorEmail | string,null | false | If available, email of the user who created the project | |
| projectCreatorId | string,null | false | If available, ID of the creator of the project | |
| projectCreatorName | string,null | false | If available, name of the user who created the project | |
| projectId | string,null | true | If available, the project id used for this model | |
| projectName | string,null | true | If available, the project name for this model | |
| scoringCode | ModelPackageScoringCodeMeta | true | If available, information about the model's scoring code | |
| useCaseDetails | UseCaseDetails | false | Details of the use-case associated to this registered model version |
ModelPackageTarget
{
"description": "target information for the model package",
"properties": {
"classCount": {
"description": "Number of classes for classification models.",
"minimum": 0,
"type": [
"integer",
"null"
]
},
"classNames": {
"description": "Class names for prediction results. When target type is Binary, two class names are returned. The first element is the minority (positive) class and the second element is the majority (negative) class. Limited to 100 returned for Multiclass.",
"items": {
"type": "string"
},
"maxItems": 100,
"type": "array"
},
"name": {
"description": "name of the target column",
"type": "string"
},
"predictionProbabilitiesColumn": {
"description": "Field or column name containing prediction probabilities",
"type": [
"string",
"null"
]
},
"predictionThreshold": {
"description": "Prediction threshold used for binary classification models",
"maximum": 1,
"minimum": 0,
"type": [
"number",
"null"
]
},
"type": {
"description": "Target type of the model.",
"enum": [
"Binary",
"Regression",
"Multiclass",
"Multilabel",
"TextGeneration",
"GeoPoint",
"AgenticWorkflow"
],
"type": "string"
}
},
"required": [
"classCount",
"classNames",
"name",
"predictionProbabilitiesColumn",
"predictionThreshold",
"type"
],
"type": "object"
}
target information for the model package
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| classCount | integer,null | true | minimum: 0 |
Number of classes for classification models. |
| classNames | [string] | true | maxItems: 100 |
Class names for prediction results. When target type is Binary, two class names are returned. The first element is the minority (positive) class and the second element is the majority (negative) class. Limited to 100 returned for Multiclass. |
| name | string | true | name of the target column | |
| predictionProbabilitiesColumn | string,null | true | Field or column name containing prediction probabilities | |
| predictionThreshold | number,null | true | maximum: 1 minimum: 0 |
Prediction threshold used for binary classification models |
| type | string | true | Target type of the model. |
Enumerated Values¶
| Property | Value |
|---|---|
| type | [Binary, Regression, Multiclass, Multilabel, TextGeneration, GeoPoint, AgenticWorkflow] |
ModelPackageTimeseries
{
"description": "time series information for the model package",
"properties": {
"datetimeColumnFormat": {
"description": "Date format for forecast date and forecast point column",
"type": [
"string",
"null"
]
},
"datetimeColumnName": {
"description": "Name of the forecast date column",
"type": [
"string",
"null"
]
},
"effectiveFeatureDerivationWindowEnd": {
"description": "Same concept as `featureDerivationWindowEnd` which is chosen by the user and based on the initial sampled data from the eda sample. When the dataset goes through aim, the pipeline reads the full dataset and figures out the \"real\" FDW (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW.",
"maximum": 0,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.25"
},
"effectiveFeatureDerivationWindowStart": {
"description": "Same concept as `featureDerivationWindowStart` which is chosen by the user and based on the initial sampled data from the eda sample. When the dataset goes through aim, the pipeline reads the full dataset and figures out the \"real\" FDW (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW.",
"maximum": 0,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.25"
},
"featureDerivationWindowEnd": {
"description": "Negative number or zero defining how many time units of the forecast distances time unit into the past relative to the forecast point the feature derivation window should end.",
"maximum": 0,
"type": [
"integer",
"null"
]
},
"featureDerivationWindowStart": {
"description": "Negative number or zero defining how many time units of the forecast distances time unit into the past relative to the forecast point the feature derivation window should begin.",
"maximum": 0,
"type": [
"integer",
"null"
]
},
"forecastDistanceColumnName": {
"description": "Name of the forecast distance column",
"type": [
"string",
"null"
]
},
"forecastDistances": {
"description": "List of integer forecast distances",
"items": {
"type": "integer"
},
"type": "array"
},
"forecastDistancesTimeUnit": {
"description": "The time unit of forecast distances",
"enum": [
"MICROSECOND",
"MILLISECOND",
"SECOND",
"MINUTE",
"HOUR",
"DAY",
"WEEK",
"MONTH",
"QUARTER",
"YEAR"
],
"type": [
"string",
"null"
]
},
"forecastPointColumnName": {
"description": "Name of the forecast point column",
"type": [
"string",
"null"
]
},
"isCrossSeries": {
"description": "true if the model is cross-series.",
"type": [
"boolean",
"null"
]
},
"isNewSeriesSupport": {
"description": "true if the model is optimized to support new series.",
"type": [
"boolean",
"null"
],
"x-versionadded": "v2.25"
},
"isTraditionalTimeSeries": {
"description": "true if the model is traditional time series.",
"type": [
"boolean",
"null"
]
},
"seriesColumnName": {
"description": "Name of the series column in case of multi-series date",
"type": [
"string",
"null"
]
}
},
"required": [
"datetimeColumnFormat",
"datetimeColumnName",
"effectiveFeatureDerivationWindowEnd",
"effectiveFeatureDerivationWindowStart",
"featureDerivationWindowEnd",
"featureDerivationWindowStart",
"forecastDistanceColumnName",
"forecastDistances",
"forecastDistancesTimeUnit",
"forecastPointColumnName",
"isCrossSeries",
"isNewSeriesSupport",
"isTraditionalTimeSeries",
"seriesColumnName"
],
"type": "object"
}
time series information for the model package
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| datetimeColumnFormat | string,null | true | Date format for forecast date and forecast point column | |
| datetimeColumnName | string,null | true | Name of the forecast date column | |
| effectiveFeatureDerivationWindowEnd | integer,null | true | maximum: 0 |
Same concept as featureDerivationWindowEnd which is chosen by the user and based on the initial sampled data from the eda sample. When the dataset goes through aim, the pipeline reads the full dataset and figures out the "real" FDW (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW. |
| effectiveFeatureDerivationWindowStart | integer,null | true | maximum: 0 |
Same concept as featureDerivationWindowStart which is chosen by the user and based on the initial sampled data from the eda sample. When the dataset goes through aim, the pipeline reads the full dataset and figures out the "real" FDW (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW. |
| featureDerivationWindowEnd | integer,null | true | maximum: 0 |
Negative number or zero defining how many time units of the forecast distances time unit into the past relative to the forecast point the feature derivation window should end. |
| featureDerivationWindowStart | integer,null | true | maximum: 0 |
Negative number or zero defining how many time units of the forecast distances time unit into the past relative to the forecast point the feature derivation window should begin. |
| forecastDistanceColumnName | string,null | true | Name of the forecast distance column | |
| forecastDistances | [integer] | true | List of integer forecast distances | |
| forecastDistancesTimeUnit | string,null | true | The time unit of forecast distances | |
| forecastPointColumnName | string,null | true | Name of the forecast point column | |
| isCrossSeries | boolean,null | true | true if the model is cross-series. | |
| isNewSeriesSupport | boolean,null | true | true if the model is optimized to support new series. | |
| isTraditionalTimeSeries | boolean,null | true | true if the model is traditional time series. | |
| seriesColumnName | string,null | true | Name of the series column in case of multi-series date |
Enumerated Values¶
| Property | Value |
|---|---|
| forecastDistancesTimeUnit | [MICROSECOND, MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR] |
ModelResponse
{
"description": "Model of the challenger.",
"properties": {
"datasetName": {
"description": "Name of dataset used to train challenger model",
"type": "string"
},
"description": {
"description": "Description of the model.",
"type": "string"
},
"executionType": {
"description": "Type of the current model.",
"type": "string"
},
"id": {
"description": "ID of the current model.",
"type": "string"
},
"isDeprecated": {
"description": "Whether the current model is deprecated model. eg. python2 based model.",
"type": "boolean",
"x-versionadded": "v2.29"
},
"name": {
"description": "Name of the model.",
"type": "string"
},
"projectId": {
"description": "Project ID of the current model.",
"type": "string"
},
"projectName": {
"description": "Project name of the current model.",
"type": "string"
}
},
"required": [
"datasetName",
"description",
"executionType",
"id",
"isDeprecated",
"name",
"projectId",
"projectName"
],
"type": "object"
}
Model of the challenger.
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| datasetName | string | true | Name of dataset used to train challenger model | |
| description | string | true | Description of the model. | |
| executionType | string | true | Type of the current model. | |
| id | string | true | ID of the current model. | |
| isDeprecated | boolean | true | Whether the current model is deprecated model. eg. python2 based model. | |
| name | string | true | Name of the model. | |
| projectId | string | true | Project ID of the current model. | |
| projectName | string | true | Project name of the current model. |
PredictionEnvironmentResponse
{
"description": "Prediction environment used by the challenger",
"properties": {
"id": {
"description": "ID of the prediction environment.",
"type": "string"
},
"name": {
"description": "Name of the prediction environment.",
"type": "string"
}
},
"required": [
"id",
"name"
],
"type": "object"
}
Prediction environment used by the challenger
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| id | string | true | ID of the prediction environment. | |
| name | string | true | Name of the prediction environment. |
Schedule
{
"description": "The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False.",
"properties": {
"dayOfMonth": {
"description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.",
"items": {
"enum": [
"*",
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23,
24,
25,
26,
27,
28,
29,
30,
31
],
"type": [
"number",
"string"
]
},
"maxItems": 31,
"type": "array"
},
"dayOfWeek": {
"description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.",
"items": {
"enum": [
"*",
0,
1,
2,
3,
4,
5,
6,
"sunday",
"SUNDAY",
"Sunday",
"monday",
"MONDAY",
"Monday",
"tuesday",
"TUESDAY",
"Tuesday",
"wednesday",
"WEDNESDAY",
"Wednesday",
"thursday",
"THURSDAY",
"Thursday",
"friday",
"FRIDAY",
"Friday",
"saturday",
"SATURDAY",
"Saturday",
"sun",
"SUN",
"Sun",
"mon",
"MON",
"Mon",
"tue",
"TUE",
"Tue",
"wed",
"WED",
"Wed",
"thu",
"THU",
"Thu",
"fri",
"FRI",
"Fri",
"sat",
"SAT",
"Sat"
],
"type": [
"number",
"string"
]
},
"maxItems": 7,
"type": "array"
},
"hour": {
"description": "The hour(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every hour of the day or ``[0 ... 23]``.",
"items": {
"enum": [
"*",
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23
],
"type": [
"number",
"string"
]
},
"maxItems": 24,
"type": "array"
},
"minute": {
"description": "The minute(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every minute of the day or``[0 ... 59]``.",
"items": {
"enum": [
"*",
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23,
24,
25,
26,
27,
28,
29,
30,
31,
32,
33,
34,
35,
36,
37,
38,
39,
40,
41,
42,
43,
44,
45,
46,
47,
48,
49,
50,
51,
52,
53,
54,
55,
56,
57,
58,
59
],
"type": [
"number",
"string"
]
},
"maxItems": 60,
"type": "array"
},
"month": {
"description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.",
"items": {
"enum": [
"*",
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
"january",
"JANUARY",
"January",
"february",
"FEBRUARY",
"February",
"march",
"MARCH",
"March",
"april",
"APRIL",
"April",
"may",
"MAY",
"May",
"june",
"JUNE",
"June",
"july",
"JULY",
"July",
"august",
"AUGUST",
"August",
"september",
"SEPTEMBER",
"September",
"october",
"OCTOBER",
"October",
"november",
"NOVEMBER",
"November",
"december",
"DECEMBER",
"December",
"jan",
"JAN",
"Jan",
"feb",
"FEB",
"Feb",
"mar",
"MAR",
"Mar",
"apr",
"APR",
"Apr",
"jun",
"JUN",
"Jun",
"jul",
"JUL",
"Jul",
"aug",
"AUG",
"Aug",
"sep",
"SEP",
"Sep",
"oct",
"OCT",
"Oct",
"nov",
"NOV",
"Nov",
"dec",
"DEC",
"Dec"
],
"type": [
"number",
"string"
]
},
"maxItems": 12,
"type": "array"
}
},
"required": [
"dayOfMonth",
"dayOfWeek",
"hour",
"minute",
"month"
],
"type": "object"
}
The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False.
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| dayOfMonth | [number,string] | true | maxItems: 31 |
The date(s) of the month that the job will run. Allowed values are either [1 ... 31] or ["*"] for all days of the month. This field is additive with dayOfWeek, meaning the job will run both on the date(s) defined in this field and the day specified by dayOfWeek (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If dayOfMonth is set to ["*"] and dayOfWeek is defined, the scheduler will trigger on every day of the month that matches dayOfWeek (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored. |
| dayOfWeek | [number,string] | true | maxItems: 7 |
The day(s) of the week that the job will run. Allowed values are [0 .. 6], where (Sunday=0), or ["*"], for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., "sunday", "Sunday", "sun", or "Sun", all map to [0]. This field is additive with dayOfMonth, meaning the job will run both on the date specified by dayOfMonth and the day defined in this field. |
| hour | [number,string] | true | maxItems: 24 |
The hour(s) of the day that the job will run. Allowed values are either ["*"] meaning every hour of the day or [0 ... 23]. |
| minute | [number,string] | true | maxItems: 60 |
The minute(s) of the day that the job will run. Allowed values are either ["*"] meaning every minute of the day or[0 ... 59]. |
| month | [number,string] | true | maxItems: 12 |
The month(s) of the year that the job will run. Allowed values are either [1 ... 12] or ["*"] for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., "jan" or "october"). Months that are not compatible with dayOfMonth are ignored, for example {"dayOfMonth": [31], "month":["feb"]}. |
UseCaseDetails
{
"description": "Details of the use-case associated to this registered model version",
"properties": {
"createdAt": {
"description": "Time when use-case was created",
"type": "string"
},
"creatorEmail": {
"description": "Email of the user who created use-case",
"type": [
"string",
"null"
]
},
"creatorId": {
"description": "ID of the creator of the use-case",
"type": "string"
},
"creatorName": {
"description": "Name of the user who created use-case",
"type": [
"string",
"null"
]
},
"id": {
"description": "ID of the associated use-case",
"type": "string"
},
"name": {
"description": "Name of the use case at the moment of creation",
"type": [
"string",
"null"
]
}
},
"required": [
"createdAt",
"creatorId",
"id"
],
"type": "object"
}
Details of the use-case associated to this registered model version
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| createdAt | string | true | Time when use-case was created | |
| creatorEmail | string,null | false | Email of the user who created use-case | |
| creatorId | string | true | ID of the creator of the use-case | |
| creatorName | string,null | false | Name of the user who created use-case | |
| id | string | true | ID of the associated use-case | |
| name | string,null | false | Name of the use case at the moment of creation |
UserMetadata
{
"description": "Information on the user who last modified the registered model",
"properties": {
"email": {
"description": "Email of the user",
"type": [
"string",
"null"
]
},
"id": {
"description": "ID of the user",
"type": "string"
},
"name": {
"description": "Full name of the user",
"type": [
"string",
"null"
]
}
},
"required": [
"email",
"id",
"name"
],
"type": "object"
}
Information on the user who last modified the registered model
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| string,null | true | Email of the user | ||
| id | string | true | ID of the user | |
| name | string,null | true | Full name of the user |