Watson OpenScale - IBM Cloud API Docs

Introduction

IBM Watson OpenScale is an enterprise-grade environment for AI infused applications that provides enterprises with visibility into how AI is being built, used, and delivering ROI – at the scale of their business.

For details about using IBM Watson OpenScale, see the docs.

Authentication

You authenticate to the API by using IAM. You can pass either a bearer token in an Authorization header or an apikey. Tokens support authenticated requests without embedding service credentials in every call. API keys use basic authentication. Learn more about IAM.

IAM authentication. Replace {token} with the bearer token returned by the IAM authentication service.

curl -X GET --header "Authorization: Bearer {Token}" "https://api.aiopenscale.cloud.ibm.com/v1/data_marts/{data_mart_id}/deployment_metrics?version=2018-09-17"

curl -X GET --header "Authorization: Bearer {Token}" "https://api.aiopenscale.cloud.ibm.com/v1/data_marts/{data_mart_id}/deployment_metrics?version=2018-09-17"

Versioning

API requests require a version parameter that takes a date in the format version=YYYY-MM-DD. When we change the API in a backwards-incompatible way, we release a new version date.

Send the version parameter with every API request. The service uses the API version for the date you specify, or the most recent version before that date. Don't default to the current date. Instead, specify a date that matches a version that is compatible with your app, and don't change it until your app is ready for a later version.

This documentation describes the current version of IBM Watson OpenScale, 2018-09-17. In some cases, differences in earlier versions are noted in the descriptions of parameters and response models.

Error handling

The IBM Watson OpenScale service uses standard HTTP response codes to indicate indicate if a method completed successfully.A 200 HTTP response always indicates success. HTTP response codes with the format 4xx indicate a failure. A 500 HTTP response code usually indicates an internal system error that cannot be resolved by the user.

ErrorResponse

Name	Description
code `integer`	HTTP error code.
error `string`	Human-readable error string, like 'Invalid image file'.

ErrorAuthentication

Name	Description
status `string`	The status of error.
statusInfo `string`	Information about the error.

ErrorHTML

Name	Description
Error `string`	HTML description of the error.

ErrorInfo

Information about what might have caused a failure, such as an image that is too large. Not returned when there is no error.

Name	Description
code `integer`	HTTP error code.
description `string`	Human-readable error description. For example, `File size limit exceeded`.
error_id `string`	Codified error string. For example, `limit_exceeded`.

Data labels

You can remove customer data if you associate the customer and the data when you send the information to a service. First you label the data with a customer ID, and then you can delete the data by the ID. Labeling data is used only by methods that accept customer data. For more information about {{site.data.keyword.aiopenscaleshort}} and labeling data, see Information security.

Use the X-Watson-Metadata header to associate a customer ID with the data. By adding a customer ID to a request, you indicate that it contains data that belongs to that customer.

Specify a random or generic string for the customer ID. Do not include personal data, such as an email address. Pass the string customer_id={id} as the argument of the header.
Use the Delete labeled data method to remove data that is associated with a customer ID.

Data collection

By default, all IBM services log requests and their results. Logging is done only to improve the services for future users. The logged data is not shared or made public.

To prevent IBM from accessing your data for general service improvements, set the X-Watson-Learning-Opt-Out request header to true for all requests. (Any value other than false or 0 disables request logging for that call.) You must set the header on each request that you do not want IBM to access for general service improvements.

Example request

curl -u "apikey:{apikey}" -H "X-Watson-Learning-Opt-Out: true" "https://gateway.watsonplatform.net/ai-openscale/api/v1/{method}"
curl -u "apikey:{apikey}" -H "X-Watson-Learning-Opt-Out: true" "https://gateway.watsonplatform.net/ai-openscale/api/v1/{method}"

Publish Scoring Payload

Publish scoring payload for a given deployment to OpenScale DataMart. It will be stored in database asynchronously.

POST /v1/data_marts/{data_mart_id}/scoring_payloads

/v1/data_marts/{data_mart_id}/scoring_payloads

Request

Path Parameters

data_mart_id
string
OpenScale DataMart ID - the service instance ID

Example: 9fdaa46c-c124-4700-b794-6d6a5262c3aa

Request Body

ScoringPayloadRequestObject[]

binding_id
string
subscription_id
string
deployment_id
string
request
Example:
{ "fields": [ "name", "age", "position" ], "values": [ [ "john", 33, "engineer" ], [ "mike", 23, "student" ] ], "meta": { "fields": [ "transactionid", "misc1" ], "values": [ [ "tran1345", "misc value for John" ], [ "tran1345", "misc value for Mike" ] ] } }
{ "fields": [ "name", "age", "position" ], "values": [ [ "john", 33, "engineer" ], [ "mike", 23, "student" ] ], "meta": { "fields": [ "transactionid", "misc1" ], "values": [ [ "tran1345", "misc value for John" ], [ "tran1345", "misc value for Mike" ] ] } }
response
Example:
{ "fields": [ "name", "age", "position", "prediction", "probability" ], "values": [ [ "john", 33, "engineer", "personal", [ 0.6744664422398081, 0.32553355776019194 ] ], [ "mike", 23, "student", "camping", [ 0.2794765664946941, 0.7205234335053059 ] ] ] }
{ "fields": [ "name", "age", "position", "prediction", "probability" ], "values": [ [ "john", 33, "engineer", "personal", [ 0.6744664422398081, 0.32553355776019194 ] ], [ "mike", 23, "student", "camping", [ 0.2794765664946941, 0.7205234335053059 ] ] ] }
scoring_id
string
scoring_timestamp
string
response_time
double
asset_revision
string
multiple_records
boolean
This property is important for payload logging for unstructered data. There will be one record created in payload logging table if this property is set to false. The request.values field and response.values field are treated as one scoring request and one scoring response in such case. If this field is set to true then it might be created more than one row in the payload logging table. The first dimension of the request.values and response.values correponds to the separate entry in the payload logging table in such case.

Default: true

```
curl -X POST --header "Authorization&#58; Bearer {apikey-token}" -H "Content-Type&#58; application/json" -d '[{"binding_id"&#58; "0c2fac72-3454-4a99-aaf2-9032de7c5007","subscription_id"&#58; "d4488363-2b31-4d0e-92a5-e80797b58114","deployment_id"&#58;"6b416e3b-8bdd-46de-b5a2-8c4d32ddaf09","request"&#58;{"fields"&#58; ["name","age","position"],"values"&#58; [["john",33,"engineer"],["mike",23,"student"]],"meta"&#58; {"fields"&#58; ["transactionid","misc1"],"values"&#58; [["tran1345","misc value for John"],["tran1345","misc value for Mike"]]}},"response"&#58;{"fields"&#58; ["name","age","position","prediction","probability"],"values"&#58; [["john",33,"engineer","personal",[0.6744664422398081,0.32553355776019194]],["mike",23,"student","camping",[0.2794765664946941,0.7205234335053059]]]}}]' "https://api.aiopenscale.cloud.ibm.com/v1/data_marts/{data_mart_id}/scoring_payloads?version=2018-09-17"
```
curl -X POST --header "Authorization: Bearer {apikey-token}" -H "Content-Type: application/json" -d '[{"binding_id": "0c2fac72-3454-4a99-aaf2-9032de7c5007","subscription_id": "d4488363-2b31-4d0e-92a5-e80797b58114","deployment_id":"6b416e3b-8bdd-46de-b5a2-8c4d32ddaf09","request":{"fields": ["name","age","position"],"values": [["john",33,"engineer"],["mike",23,"student"]],"meta": {"fields": ["transactionid","misc1"],"values": [["tran1345","misc value for John"],["tran1345","misc value for Mike"]]}},"response":{"fields": ["name","age","position","prediction","probability"],"values": [["john",33,"engineer","personal",[0.6744664422398081,0.32553355776019194]],["mike",23,"student","camping",[0.2794765664946941,0.7205234335053059]]]}}]' "https://api.aiopenscale.cloud.ibm.com/v1/data_marts/{data_mart_id}/scoring_payloads?version=2018-09-17"

Response

Response Body

ScoringPayloadRequestObject[]

binding_id
string
subscription_id
string
deployment_id
string
request
Example:
{ "fields": [ "name", "age", "position" ], "values": [ [ "john", 33, "engineer" ], [ "mike", 23, "student" ] ], "meta": { "fields": [ "transactionid", "misc1" ], "values": [ [ "tran1345", "misc value for John" ], [ "tran1345", "misc value for Mike" ] ] } }
{ "fields": [ "name", "age", "position" ], "values": [ [ "john", 33, "engineer" ], [ "mike", 23, "student" ] ], "meta": { "fields": [ "transactionid", "misc1" ], "values": [ [ "tran1345", "misc value for John" ], [ "tran1345", "misc value for Mike" ] ] } }
response
Example:
{ "fields": [ "name", "age", "position", "prediction", "probability" ], "values": [ [ "john", 33, "engineer", "personal", [ 0.6744664422398081, 0.32553355776019194 ] ], [ "mike", 23, "student", "camping", [ 0.2794765664946941, 0.7205234335053059 ] ] ] }
{ "fields": [ "name", "age", "position", "prediction", "probability" ], "values": [ [ "john", 33, "engineer", "personal", [ 0.6744664422398081, 0.32553355776019194 ] ], [ "mike", 23, "student", "camping", [ 0.2794765664946941, 0.7205234335053059 ] ] ] }
scoring_id
string
scoring_timestamp
string
response_time
double
asset_revision
string
multiple_records
boolean
This property is important for payload logging for unstructered data. There will be one record created in payload logging table if this property is set to false. The request.values field and response.values field are treated as one scoring request and one scoring response in such case. If this field is set to true then it might be created more than one row in the payload logging table. The first dimension of the request.values and response.values correponds to the separate entry in the payload logging table in such case.

Status Code

202
Accepted

Example responses

Status 202

[
  {
    "request": {
      "fields": [
        "name",
        "age",
        "position"
      ],
      "values": [
        [
          "john",
          33,
          "engineer"
        ],
        [
          "mike",
          23,
          "student"
        ]
      ],
      "meta": {
        "fields": [
          "transactionid",
          "misc1"
        ],
        "values": [
          [
            "tran1345",
            "misc value for John"
          ],
          [
            "tran1345",
            "misc value for Mike"
          ]
        ]
      }
    },
    "binding_id": "0c2fac72-3454-4a99-aaf2-9032de7c5007",
    "response": {
      "fields": [
        "name",
        "age",
        "position",
        "prediction",
        "probability"
      ],
      "values": [
        [
          "john",
          33,
          "engineer",
          "personal",
          [
            0.6744664422398081,
            0.32553355776019194
          ]
        ],
        [
          "mike",
          23,
          "student",
          "camping",
          [
            0.2794765664946941,
            0.7205234335053059
          ]
        ]
      ]
    },
    "subscription_id": "d4488363-2b31-4d0e-92a5-e80797b58114",
    "deployment_id": "6b416e3b-8bdd-46de-b5a2-8c4d32ddaf09"
  }
][
  {
    "request": {
      "fields": [
        "name",
        "age",
        "position"
      ],
      "values": [
        [
          "john",
          33,
          "engineer"
        ],
        [
          "mike",
          23,
          "student"
        ]
      ],
      "meta": {
        "fields": [
          "transactionid",
          "misc1"
        ],
        "values": [
          [
            "tran1345",
            "misc value for John"
          ],
          [
            "tran1345",
            "misc value for Mike"
          ]
        ]
      }
    },
    "binding_id": "0c2fac72-3454-4a99-aaf2-9032de7c5007",
    "response": {
      "fields": [
        "name",
        "age",
        "position",
        "prediction",
        "probability"
      ],
      "values": [
        [
          "john",
          33,
          "engineer",
          "personal",
          [
            0.6744664422398081,
            0.32553355776019194
          ]
        ],
        [
          "mike",
          23,
          "student",
          "camping",
          [
            0.2794765664946941,
            0.7205234335053059
          ]
        ]
      ]
    },
    "subscription_id": "d4488363-2b31-4d0e-92a5-e80797b58114",
    "deployment_id": "6b416e3b-8bdd-46de-b5a2-8c4d32ddaf09"
  }
]

Post Feedback Payload

Upload feedback payload.

POST /v1/data_marts/{data_mart_id}/feedback_payloads

/v1/data_marts/{data_mart_id}/feedback_payloads

Request

Path Parameters

data_mart_id
string
OpenScale DataMart ID - the service instance ID

Example: 9fdaa46c-c124-4700-b794-6d6a5262c3aa

Request Body

FeedbackPayloadRequestObject

Example:

{
  "binding_id": "377ba11a-d6c1-11e8-9f8b-f2801f1b9fd1",
  "subscription_id": "3bb41316-d6c1-11e8-9f8b-f2801f1b9fd1",
  "fields": [
    "name",
    "age",
    "position"
  ],
  "values": [
    [
      "john",
      33,
      "engineer"
    ],
    [
      "mike",
      23,
      "student"
    ]
  ]
}{
  "binding_id": "377ba11a-d6c1-11e8-9f8b-f2801f1b9fd1",
  "subscription_id": "3bb41316-d6c1-11e8-9f8b-f2801f1b9fd1",
  "fields": [
    "name",
    "age",
    "position"
  ],
  "values": [
    [
      "john",
      33,
      "engineer"
    ],
    [
      "mike",
      23,
      "student"
    ]
  ]
}

```
curl -X POST --header "Authorization&#58; Bearer {apikey-token}" -H "Content-Type&#58; application/json" -d '{"binding_id"&#58; "377ba11a-d6c1-11e8-9f8b-f2801f1b9fd1","subscription_id"&#58; "3bb41316-d6c1-11e8-9f8b-f2801f1b9fd1","fields"&#58; ["name","age","position"],"values"&#58; [["john",33,"engineer"],["mike",23,"student"]]}' "https://api.aiopenscale.cloud.ibm.com/v1/data_marts/{data_mart_id}/feedback_payloads?version=2018-09-17"
```
curl -X POST --header "Authorization: Bearer {apikey-token}" -H "Content-Type: application/json" -d '{"binding_id": "377ba11a-d6c1-11e8-9f8b-f2801f1b9fd1","subscription_id": "3bb41316-d6c1-11e8-9f8b-f2801f1b9fd1","fields": ["name","age","position"],"values": [["john",33,"engineer"],["mike",23,"student"]]}' "https://api.aiopenscale.cloud.ibm.com/v1/data_marts/{data_mart_id}/feedback_payloads?version=2018-09-17"

Response

Status Code

204
Feedback registered

No Sample Response

This method does not specify any sample responses.

Get Feedback

Get feedback data.

GET /v1/data_marts/{data_mart_id}/service_bindings/{binding_id}/subscriptions/{subscription_id}/feedback_payloads

/v1/data_marts/{data_mart_id}/service_bindings/{binding_id}/subscriptions/{subscription_id}/feedback_payloads

Request

Custom Headers

Accept
string
Allowable values: [application/json,text/csv]

Path Parameters

data_mart_id
string
OpenScale DataMart ID - the service instance ID

Example: 9fdaa46c-c124-4700-b794-6d6a5262c3aa
binding_id
string
subscription_id
string

Query Parameters

header
boolean
if true header will be first line of the csv content

Default: false
delimiter
string
delimiter character for csv data

Default: ,

Response

Response Body

object[]

Status Code

200
response

Example responses

Log Feedback

Send feedback data to the service.

POST /v1/data_marts/{data_mart_id}/service_bindings/{binding_id}/subscriptions/{subscription_id}/feedback_payloads

/v1/data_marts/{data_mart_id}/service_bindings/{binding_id}/subscriptions/{subscription_id}/feedback_payloads

Request

Custom Headers

Content-Type
string
Allowable values: [application/json,text/csv]

Path Parameters

data_mart_id
string
OpenScale DataMart ID - the service instance ID

Example: 9fdaa46c-c124-4700-b794-6d6a5262c3aa
binding_id
string
subscription_id
string

Query Parameters

header
boolean
if true header will be first line of the csv content

Default: false
delimiter
string
delimiter character for csv data

Default: ,
on_error
string
expected behaviour on error

Allowable values: [stop,continue]
Default: stop
skip
integer
skip number of rows from input
limit
integer
limit for number of processed input rows

Request Body

object[]

Response

Response Body

FeedbackUploadResponseObject

Status Code

200
response

No Sample Response

This method does not specify any sample responses.

Post Explanation Task

Submit task for computing explanation of a prediction

POST /v1/data_marts/{data_mart_id}/explanation_tasks

/v1/data_marts/{data_mart_id}/explanation_tasks

Request

Path Parameters

data_mart_id
string
OpenScale DataMart ID - the service instance ID

Example: 9fdaa46c-c124-4700-b794-6d6a5262c3aa

Request Body

ExplanationTaskRequest

```
curl -X POST --header "Authorization&#58; Bearer {apikey-token}" -H "Content-Type&#58; application/json" -d '{scoring_id&#58; "22dujd9we20ddlw22"}' "https://api.aiopenscale.cloud.ibm.com/v1/data_marts/{data_mart_id}/explanation_tasks?version=2018-09-17"
```

Response

Response Body

PostExplanationTaskResponse

Status Code

202
Explanation request submitted successfully

No Sample Response

This method does not specify any sample responses.

Get Explanation Task

Get explanation for the given explanation task id

GET /v1/data_marts/{data_mart_id}/explanation_tasks/{explanation_task_id}

/v1/data_marts/{data_mart_id}/explanation_tasks/{explanation_task_id}

Request

Path Parameters

data_mart_id
string
OpenScale DataMart ID - the service instance ID

Example: 9fdaa46c-c124-4700-b794-6d6a5262c3aa
explanation_task_id
string
ID of the explanation task

Example: 9fdaa46c-c124-4700-b794-6d6a5262c3aa

```
curl -X GET --header "Authorization&#58; Bearer {apikey-token}" "https://api.aiopenscale.cloud.ibm.com/v1/data_marts/{data_mart_id}/explanation_tasks/{explanation_task_id}?version=2018-09-17"
```

Response

Response Body

GetExplanationTaskResponse

Status Code

200
Explanation successfully retrieved

No Sample Response

This method does not specify any sample responses.

Post fairness monitoring

Computes the bias mitigation/remediation for the specified model. The fairness monitoring debias request payload details must be valid. Makes an online prediction and de-bias prediction on a given data values.

The de-bias request payload takes a batch of input rows which should have model fields and values, in the following format.

{
    "fields": [
        "name",
        "age",
        "position"
    ],
    "values": [
        [
        "john",
        33,
        "engineer"
        ],
        [
        "mike",
        23,
        "student"
        ]
    ]
}

The response of this API would contain regular prediction columns provided by the underlying machine learning model engine, and along with that would contain de-biased predictions as listed in the following format.

{
    "fields": [
        "name",
        "age",
        "position",
        "prediction",
        "probability",
        "debiased_prediction",
        "debiased_probability",
        "debiased_decoded_target"
    ],
    "values": [
        [
            "john",
            35,
            "engineer",
            0.3,
            [
                0.754601226993865,
                0.24539877300613497
            ],
            0.4,
            [
                0.898098798985,
                0.49877
            ],
            "good"
        ],
        [
            "mike",
            23,
            "student",
            0.5,
            [
                0.2794765664946941,
                0.7205234335053059
            ],
            0.6,
            [
                0.407098,
                0.86756785758667
            ],
            "bad"
        ]
    ]
}

POST /v1/data_marts/{data_mart_id}/service_bindings/{binding_id}/subscriptions/{subscription_id}/deployments/{deployment_id}/online

/v1/data_marts/{data_mart_id}/service_bindings/{binding_id}/subscriptions/{subscription_id}/deployments/{deployment_id}/online

Request

Custom Headers

Authorization
string
Identity Access Management (IAM) bearer token.
X-Global-Transaction-Id
string
The transaction ID/scoring ID to the given payload.

Path Parameters

data_mart_id
string
AIOS OpenScale DataMart ID - the service instance ID.
binding_id
string
The Model engine binding ID.
subscription_id
string
The subscription ID.
deployment_id
string
The deployment ID.

Request Body

FairnessMonitoringRemediationRequest

fields
string[]
The fields to process debias scoring
Example:
[ "name", "age", "position" ]
values
string[]
The values associated to the fields
Example:
[ [ "john", 33, "engineer" ] ]

```
curl -X POST --header "Authorization&#58; Bearer {apikey-token}" -H "Content-Type&#58; application/json" -d '{"fields"&#58; [ "name", "age","position" ], "values"&#58; [["john", 33, "engineer" ], [ "mike", 23, "student" ] ] }' "https://api.aiopenscale.cloud.ibm.com/v1/data_marts/{data_mart_id}/service_bindings/{binding_id}/subscriptions/{subscription_id}/deployments/{deployment_id}/online?version=2018-09-17"
```
curl -X POST --header "Authorization: Bearer {apikey-token}" -H "Content-Type: application/json" -d '{"fields": [ "name", "age","position" ], "values": [["john", 33, "engineer" ], [ "mike", 23, "student" ] ] }' "https://api.aiopenscale.cloud.ibm.com/v1/data_marts/{data_mart_id}/service_bindings/{binding_id}/subscriptions/{subscription_id}/deployments/{deployment_id}/online?version=2018-09-17"

Response

Response Body

FairnessMonitoringRemediationResponse

fields
object
The fields of the model processed debias scoring.
Example:
[ "name", "age", "position", "prediction", "probability", "debiased_prediction", "debiased_probability", "debiased_decoded_target" ]
values
object
The values associated to the fields.
Example:
[ [ "john", 35, "engineer", 0.3, [ 0.754601226993865, 0.24539877300613497 ], 0.4, [ 0.754601226993865, 0.24539877300613497 ], "good" ] ]

Status Code

200
Accepted
401
You are not authorized to perform this action.
403
You are not permitted to perform this action.
500
An service error occurred. The fairness monitoring debias was not initiated.

Example responses

Status 200

{
  "fields": [
    "name",
    "age",
    "position",
    "prediction",
    "probability",
    "debiased_prediction",
    "debiased_probability",
    "debiased_decoded_target"
  ],
  "values": [
    [
      "john",
      35,
      "engineer",
      0.3,
      [
        0.754601226993865,
        0.24539877300613497
      ],
      0.4,
      [
        0.754601226993865,
        0.24539877300613497
      ],
      "good"
    ]
  ]
}{
  "fields": [
    "name",
    "age",
    "position",
    "prediction",
    "probability",
    "debiased_prediction",
    "debiased_probability",
    "debiased_decoded_target"
  ],
  "values": [
    [
      "john",
      35,
      "engineer",
      0.3,
      [
        0.754601226993865,
        0.24539877300613497
      ],
      0.4,
      [
        0.754601226993865,
        0.24539877300613497
      ],
      "good"
    ]
  ]
}

Export subscription

Export the subscription configuration.

GET /v1/data_marts/{data_mart_id}/service_bindings/{binding_id}/subscriptions/{subscription_id}/file

/v1/data_marts/{data_mart_id}/service_bindings/{binding_id}/subscriptions/{subscription_id}/file

Request

Path Parameters

data_mart_id
string
OpenScale DataMart ID - the service instance ID

Example: 9fdaa46c-c124-4700-b794-6d6a5262c3aa
binding_id
string
subscription_id
string

Response

Response Body

SubscriptionExportObject

asset

deployments

Example:

[
  {
    "name": "WML scoring endpoint",
    "deployment_id": "b940c3a9-4d02-4201-8816-c44b51f72c99",
    "url": "https://us-south.wml.cloud.ibm.com/v3/wml_instances/c0701f36-a87e-433c-bf85-2f6b01e7ed94/deployments/b940c3a9-4d02-4201-8816-c44b51f72c99",
    "created_at": "2018-01-11T10:00:00",
    "deployment_type": "online"
  },
  {
    "name": "Azure scoring endpint",
    "deployment_id": "ae62976ad690472eaf4f9797075ed831",
    "url": "https://ussouthcentral.services.azureml.net/subscriptions/744bca722299451cb682ed6fb75fb671/services/ae62976ad690472eaf4f9797075ed831",
    "created_at": "2018-01-11T10:02:00",
    "deployment_type": "online",
    "scoring_endpoint": {
      "url": "https://ussouthcentral.services.azureml.net/subscriptions/744bca722299451cb682ed6fb75fb671/services/ae62976ad690472eaf4f9797075ed831/execute?api-version=2.0&format=swagger",
      "credentials": {
        "token": "dhgsfg3uy46rteyfugwerfygueq6wetfguyewf"
      }
    }
  },
  {
    "name": "Custom scoring endpint",
    "deployment_id": "2515a7222dgg3b682ed6fb75fb6",
    "url": "https://10.0.0.34/v1/deployments/2515a7222dgg3b682ed6fb75fb6",
    "created_at": "2018-01-11T10:05:00",
    "deployment_type": "online",
    "scoring_endpoint": {
      "url": "https://10.0.0.34/v1/deployments/2515a7222dgg3b682ed6fb75fb6/online"
    }
  }
][
  {
    "name": "WML scoring endpoint",
    "deployment_id": "b940c3a9-4d02-4201-8816-c44b51f72c99",
    "url": "https://us-south.wml.cloud.ibm.com/v3/wml_instances/c0701f36-a87e-433c-bf85-2f6b01e7ed94/deployments/b940c3a9-4d02-4201-8816-c44b51f72c99",
    "created_at": "2018-01-11T10:00:00",
    "deployment_type": "online"
  },
  {
    "name": "Azure scoring endpint",
    "deployment_id": "ae62976ad690472eaf4f9797075ed831",
    "url": "https://ussouthcentral.services.azureml.net/subscriptions/744bca722299451cb682ed6fb75fb671/services/ae62976ad690472eaf4f9797075ed831",
    "created_at": "2018-01-11T10:02:00",
    "deployment_type": "online",
    "scoring_endpoint": {
      "url": "https://ussouthcentral.services.azureml.net/subscriptions/744bca722299451cb682ed6fb75fb671/services/ae62976ad690472eaf4f9797075ed831/execute?api-version=2.0&format=swagger",
      "credentials": {
        "token": "dhgsfg3uy46rteyfugwerfygueq6wetfguyewf"
      }
    }
  },
  {
    "name": "Custom scoring endpint",
    "deployment_id": "2515a7222dgg3b682ed6fb75fb6",
    "url": "https://10.0.0.34/v1/deployments/2515a7222dgg3b682ed6fb75fb6",
    "created_at": "2018-01-11T10:05:00",
    "deployment_type": "online",
    "scoring_endpoint": {
      "url": "https://10.0.0.34/v1/deployments/2515a7222dgg3b682ed6fb75fb6/online"
    }
  }
]

export_info
asset_properties
Additional asset properties (subject of discovery if not provided when creating the subscription)
sample_payload
configurations

Status Code

200
response

Example responses

Status 200

{
  "export_info": {
    "origin": "/v1/data_marts/test-data-mart/service_bindings/test-subscription-id/subscriptions/the_test_asset_1",
    "timestamp": "2019-02-06T12:49:19.553Z",
    "api_version": "v1"
  },
  "asset_properties": {
    "runtime_environment": "spark-2.3",
    "training_data_schema": {
      "type": "struct",
      "fields": [
        {
          "name": "ID",
          "type": "integer",
          "nullable": true,
          "metadata": {
            "modeling_role": "feature"
          }
        },
        {
          "name": "Gender",
          "type": "string",
          "nullable": true,
          "metadata": {
            "measure": "discrete",
            "modeling_role": "feature"
          }
        },
        {
          "name": "Action",
          "type": "double",
          "nullable": true
        }
      ]
    },
    "label_column": "Action",
    "problem_type": "multiclass",
    "model_type": "mllib-2.3",
    "input_data_type": "structured",
    "prediction_field": "predicted_label",
    "prediction_probability_field": "score",
    "categorical_fields": [],
    "feature_fields": [
      "radius_mean",
      "texture_mean"
    ]
  },
  "configurations": {
    "payload_logging": {
      "dynamic_schema_update": true
    },
    "quality_monitoring": {
      "min_feedback_data_size": 5,
      "evaluation_definition": {
        "method": "multiclass",
        "threshold": 0.8
      }
    }
  },
  "asset": {
    "name": "My model",
    "asset_id": "the_test_asset_1",
    "url": "https://us-south.ml.cloud.ibm.com/v3/wml_instances/1e4d6841-f0b9-4d84-8649-f8dcb3342512/published_models/19a1f6a7-3bb4-4142-8379-3587f0092a37",
    "asset_type": "model",
    "created_at": "2018-10-18T17:55:43.879Z"
  },
  "deployments": [
    {
      "name": "My Deployment",
      "url": "https://us-south.ml.cloud.ibm.com/v3/wml_instances/1e4d6841-f0b9-4d84-8649-f8dcb3342512/deployments/a3a62dfd-d16b-4103-9f5f-4cfae2cdf22d",
      "deployment_type": "online",
      "created_at": "2018-10-18T17:55:43.897Z",
      "deployment_id": "a3a62dfd-d16b-4103-9f5f-4cfae2cdf22d"
    },
    {
      "name": "DEMO-multi-classification-endpoint-201810101439",
      "url": "DEMO-multi-classification-endpoint-201810101439",
      "deployment_type": "online",
      "scoring_endpoint": {
        "url": "DEMO-multi-classification-endpoint-201810101439",
        "request_headers": {
          "Content-Type": "application/json"
        }
      },
      "deployment_rn": "arn:aws:sagemaker:us-east-1:014862798213:endpoint/demo-201810101439",
      "created_at": "2018-10-10T14:39:21.421Z",
      "deployment_id": "37a83f399e6dc3b9d08d7d01fe690665"
    }
  ],
  "sample_payload": {
    "request": {
      "fields": [
        "radius_mean",
        "texture_mean"
      ],
      "values": [
        [
          17.02,
          23.98
        ],
        [
          17.02,
          23.98
        ]
      ]
    },
    "response": {
      "fields": [
        "predicted_label",
        "score"
      ],
      "values": [
        [
          1,
          [
            5.532644564709699e-9,
            1
          ]
        ],
        [
          1,
          [
            5.532644564709699e-9,
            1
          ]
        ]
      ]
    }
  }
}{
  "export_info": {
    "origin": "/v1/data_marts/test-data-mart/service_bindings/test-subscription-id/subscriptions/the_test_asset_1",
    "timestamp": "2019-02-06T12:49:19.553Z",
    "api_version": "v1"
  },
  "asset_properties": {
    "runtime_environment": "spark-2.3",
    "training_data_schema": {
      "type": "struct",
      "fields": [
        {
          "name": "ID",
          "type": "integer",
          "nullable": true,
          "metadata": {
            "modeling_role": "feature"
          }
        },
        {
          "name": "Gender",
          "type": "string",
          "nullable": true,
          "metadata": {
            "measure": "discrete",
            "modeling_role": "feature"
          }
        },
        {
          "name": "Action",
          "type": "double",
          "nullable": true
        }
      ]
    },
    "label_column": "Action",
    "problem_type": "multiclass",
    "model_type": "mllib-2.3",
    "input_data_type": "structured",
    "prediction_field": "predicted_label",
    "prediction_probability_field": "score",
    "categorical_fields": [],
    "feature_fields": [
      "radius_mean",
      "texture_mean"
    ]
  },
  "configurations": {
    "payload_logging": {
      "dynamic_schema_update": true
    },
    "quality_monitoring": {
      "min_feedback_data_size": 5,
      "evaluation_definition": {
        "method": "multiclass",
        "threshold": 0.8
      }
    }
  },
  "asset": {
    "name": "My model",
    "asset_id": "the_test_asset_1",
    "url": "https://us-south.ml.cloud.ibm.com/v3/wml_instances/1e4d6841-f0b9-4d84-8649-f8dcb3342512/published_models/19a1f6a7-3bb4-4142-8379-3587f0092a37",
    "asset_type": "model",
    "created_at": "2018-10-18T17:55:43.879Z"
  },
  "deployments": [
    {
      "name": "My Deployment",
      "url": "https://us-south.ml.cloud.ibm.com/v3/wml_instances/1e4d6841-f0b9-4d84-8649-f8dcb3342512/deployments/a3a62dfd-d16b-4103-9f5f-4cfae2cdf22d",
      "deployment_type": "online",
      "created_at": "2018-10-18T17:55:43.897Z",
      "deployment_id": "a3a62dfd-d16b-4103-9f5f-4cfae2cdf22d"
    },
    {
      "name": "DEMO-multi-classification-endpoint-201810101439",
      "url": "DEMO-multi-classification-endpoint-201810101439",
      "deployment_type": "online",
      "scoring_endpoint": {
        "url": "DEMO-multi-classification-endpoint-201810101439",
        "request_headers": {
          "Content-Type": "application/json"
        }
      },
      "deployment_rn": "arn:aws:sagemaker:us-east-1:014862798213:endpoint/demo-201810101439",
      "created_at": "2018-10-10T14:39:21.421Z",
      "deployment_id": "37a83f399e6dc3b9d08d7d01fe690665"
    }
  ],
  "sample_payload": {
    "request": {
      "fields": [
        "radius_mean",
        "texture_mean"
      ],
      "values": [
        [
          17.02,
          23.98
        ],
        [
          17.02,
          23.98
        ]
      ]
    },
    "response": {
      "fields": [
        "predicted_label",
        "score"
      ],
      "values": [
        [
          1,
          [
            5.532644564709699e-9,
            1
          ]
        ],
        [
          1,
          [
            5.532644564709699e-9,
            1
          ]
        ]
      ]
    }
  }
}

Import subscription

Import a subscription configuration.

PUT /v1/data_marts/{data_mart_id}/service_bindings/{binding_id}/subscriptions/{subscription_id}/file

/v1/data_marts/{data_mart_id}/service_bindings/{binding_id}/subscriptions/{subscription_id}/file

Request

Path Parameters

data_mart_id
string
OpenScale DataMart ID - the service instance ID

Example: 9fdaa46c-c124-4700-b794-6d6a5262c3aa
binding_id
string
subscription_id
string

Query Parameters

on_error
string
expected behaviour on error

Allowable values: [stop,continue]
Default: stop

Request Body

SubscriptionImportObject

Example:

{
  "sample_payload": {
    "request": {
      "fields": [
        "radius_mean",
        "texture_mean"
      ],
      "values": [
        [
          17.02,
          23.98
        ],
        [
          17.02,
          23.98
        ]
      ]
    },
    "response": {
      "fields": [
        "predicted_label",
        "score"
      ],
      "values": [
        [
          1,
          [
            5.532644564709699e-9,
            1
          ]
        ],
        [
          1,
          [
            5.532644564709699e-9,
            1
          ]
        ]
      ]
    }
  },
  "asset_properties": {
    "prediction_field": "predicted_label",
    "prediction_probability_field": "score",
    "label_column": "diagnosis",
    "categorical_fields": [],
    "problem_type": "multiclass",
    "feature_fields": [
      "radius_mean",
      "texture_mean"
    ],
    "input_data_type": "structured"
  },
  "configurations": {
    "payload_logging": {
      "dynamic_schema_update": true
    },
    "quality_monitoring": {
      "min_feedback_data_size": 5,
      "evaluation_definition": {
        "method": "multiclass",
        "threshold": 0.8
      }
    }
  },
  "asset": {
    "name": "DEMO-multi-classification-2018-10-10-14-26-26",
    "asset_id": "0530ab0cd4f4dd5486b19c08df8b6914",
    "url": "s3://sagemaker-us-east-1-014862798213/sagemaker/test-prediction/DEMO/output/model.tar.gz",
    "asset_type": "model",
    "created_at": "2018-10-10T14:31:44.348Z"
  },
  "deployments": [
    {
      "name": "DEMO-multi-classification-endpoint-201810101439",
      "url": "DEMO-multi-classification-endpoint-201810101439",
      "deployment_type": "online",
      "scoring_endpoint": {
        "url": "DEMO-multi-classification-endpoint-201810101439",
        "request_headers": {
          "Content-Type": "application/json"
        }
      },
      "deployment_rn": "arn:aws:sagemaker:us-east-1:014862798213:endpoint/demo-201810101439",
      "created_at": "2018-10-10T14:39:21.421Z",
      "deployment_id": "37a83f399e6dc3b9d08d7d01fe690665"
    }
  ]
}{
  "sample_payload": {
    "request": {
      "fields": [
        "radius_mean",
        "texture_mean"
      ],
      "values": [
        [
          17.02,
          23.98
        ],
        [
          17.02,
          23.98
        ]
      ]
    },
    "response": {
      "fields": [
        "predicted_label",
        "score"
      ],
      "values": [
        [
          1,
          [
            5.532644564709699e-9,
            1
          ]
        ],
        [
          1,
          [
            5.532644564709699e-9,
            1
          ]
        ]
      ]
    }
  },
  "asset_properties": {
    "prediction_field": "predicted_label",
    "prediction_probability_field": "score",
    "label_column": "diagnosis",
    "categorical_fields": [],
    "problem_type": "multiclass",
    "feature_fields": [
      "radius_mean",
      "texture_mean"
    ],
    "input_data_type": "structured"
  },
  "configurations": {
    "payload_logging": {
      "dynamic_schema_update": true
    },
    "quality_monitoring": {
      "min_feedback_data_size": 5,
      "evaluation_definition": {
        "method": "multiclass",
        "threshold": 0.8
      }
    }
  },
  "asset": {
    "name": "DEMO-multi-classification-2018-10-10-14-26-26",
    "asset_id": "0530ab0cd4f4dd5486b19c08df8b6914",
    "url": "s3://sagemaker-us-east-1-014862798213/sagemaker/test-prediction/DEMO/output/model.tar.gz",
    "asset_type": "model",
    "created_at": "2018-10-10T14:31:44.348Z"
  },
  "deployments": [
    {
      "name": "DEMO-multi-classification-endpoint-201810101439",
      "url": "DEMO-multi-classification-endpoint-201810101439",
      "deployment_type": "online",
      "scoring_endpoint": {
        "url": "DEMO-multi-classification-endpoint-201810101439",
        "request_headers": {
          "Content-Type": "application/json"
        }
      },
      "deployment_rn": "arn:aws:sagemaker:us-east-1:014862798213:endpoint/demo-201810101439",
      "created_at": "2018-10-10T14:39:21.421Z",
      "deployment_id": "37a83f399e6dc3b9d08d7d01fe690665"
    }
  ]
}

asset

deployments

Example:

[
  {
    "name": "WML scoring endpoint",
    "deployment_id": "b940c3a9-4d02-4201-8816-c44b51f72c99",
    "url": "https://us-south.wml.cloud.ibm.com/v3/wml_instances/c0701f36-a87e-433c-bf85-2f6b01e7ed94/deployments/b940c3a9-4d02-4201-8816-c44b51f72c99",
    "created_at": "2018-01-11T10:00:00",
    "deployment_type": "online"
  },
  {
    "name": "Azure scoring endpint",
    "deployment_id": "ae62976ad690472eaf4f9797075ed831",
    "url": "https://ussouthcentral.services.azureml.net/subscriptions/744bca722299451cb682ed6fb75fb671/services/ae62976ad690472eaf4f9797075ed831",
    "created_at": "2018-01-11T10:02:00",
    "deployment_type": "online",
    "scoring_endpoint": {
      "url": "https://ussouthcentral.services.azureml.net/subscriptions/744bca722299451cb682ed6fb75fb671/services/ae62976ad690472eaf4f9797075ed831/execute?api-version=2.0&format=swagger",
      "credentials": {
        "token": "dhgsfg3uy46rteyfugwerfygueq6wetfguyewf"
      }
    }
  },
  {
    "name": "Custom scoring endpint",
    "deployment_id": "2515a7222dgg3b682ed6fb75fb6",
    "url": "https://10.0.0.34/v1/deployments/2515a7222dgg3b682ed6fb75fb6",
    "created_at": "2018-01-11T10:05:00",
    "deployment_type": "online",
    "scoring_endpoint": {
      "url": "https://10.0.0.34/v1/deployments/2515a7222dgg3b682ed6fb75fb6/online"
    }
  }
][
  {
    "name": "WML scoring endpoint",
    "deployment_id": "b940c3a9-4d02-4201-8816-c44b51f72c99",
    "url": "https://us-south.wml.cloud.ibm.com/v3/wml_instances/c0701f36-a87e-433c-bf85-2f6b01e7ed94/deployments/b940c3a9-4d02-4201-8816-c44b51f72c99",
    "created_at": "2018-01-11T10:00:00",
    "deployment_type": "online"
  },
  {
    "name": "Azure scoring endpint",
    "deployment_id": "ae62976ad690472eaf4f9797075ed831",
    "url": "https://ussouthcentral.services.azureml.net/subscriptions/744bca722299451cb682ed6fb75fb671/services/ae62976ad690472eaf4f9797075ed831",
    "created_at": "2018-01-11T10:02:00",
    "deployment_type": "online",
    "scoring_endpoint": {
      "url": "https://ussouthcentral.services.azureml.net/subscriptions/744bca722299451cb682ed6fb75fb671/services/ae62976ad690472eaf4f9797075ed831/execute?api-version=2.0&format=swagger",
      "credentials": {
        "token": "dhgsfg3uy46rteyfugwerfygueq6wetfguyewf"
      }
    }
  },
  {
    "name": "Custom scoring endpint",
    "deployment_id": "2515a7222dgg3b682ed6fb75fb6",
    "url": "https://10.0.0.34/v1/deployments/2515a7222dgg3b682ed6fb75fb6",
    "created_at": "2018-01-11T10:05:00",
    "deployment_type": "online",
    "scoring_endpoint": {
      "url": "https://10.0.0.34/v1/deployments/2515a7222dgg3b682ed6fb75fb6/online"
    }
  }
]

asset_properties
Additional asset properties (subject of discovery if not provided when creating the subscription)
sample_payload
configurations

Response

Response Body

ConfigurationActionResult[]

Status Code

200
response

No Sample Response

This method does not specify any sample responses.

Introduction

Authentication

Versioning

Error handling

Data labels

Data collection

Methods

Publish Scoring Payload

Path Parameters

data_mart_id

Request Body

binding_id

subscription_id

deployment_id

request

values

fields

meta

fields

values

response

values

fields

scoring_id

scoring_timestamp

response_time

asset_revision

multiple_records

Response Body

binding_id

subscription_id

deployment_id

request

values

fields

meta

fields

values

response

values

fields

scoring_id

scoring_timestamp

response_time

asset_revision

multiple_records

Status Code

202

Post Feedback Payload

Path Parameters

data_mart_id

Request Body

binding_id

subscription_id

values

fields

Status Code

204

No Sample Response

Get Feedback

Custom Headers

Accept

Path Parameters

data_mart_id

binding_id

subscription_id

Query Parameters

header

delimiter

Response Body

Status Code

200

Log Feedback

Custom Headers

Content-Type

Path Parameters

data_mart_id

binding_id

subscription_id

Query Parameters