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Publish a model as a service (API)

This tutorial covers the process of publishing a model as a service using the Sensei Machine Learning API .

Getting started

This tutorial requires a working understanding of Adobe Experience Platform Data Science Workspace. Before beginning this tutorial, please review the Data Science Workspace overview for a high-level introduction to the service.
To follow along with this tutorial, you must have an existing ML Engine, ML Instance, and Experiment. For steps on how to create these in the API, see the tutorial on importing a packaged recipe .
Finally, before starting this tutorial, please review the getting started section of the developer guide for important information that you need to know in order to successfully make calls to the Sensei Machine Learning API, including the required headers used throughout this tutorial:
  • {ACCESS_TOKEN}
  • {IMS_ORG}
  • {API_KEY}
All POST, PUT, and PATCH requests require an additional header:
  • Content-Type: application/json

Key Terms

The following table outlines some common terminology used in this tutorial:
Term
Definition
Machine Learning Instance (ML Instance)
An instance of a Sensei Engine for a particular tenant, containing specific data, parameters, and Sensei code.
Experiment
An umbrella entity for holding training Experiment Runs, scoring Experiment Runs, or both.
Scheduled Experiment
A term to describe the automation of training or scoring Experiment Runs, governed by a user defined schedule.
Experiment Run
A particular instance of training or scoring Experiments. Multiple Experiment Runs from a particular Experiment may differ in dataset values used for training or scoring.
Trained Model
A machine learning model created by the process of experimenting and feature engineering before arriving at a validated, evaluated, and finalized model.
Published Model
A finalized and versioned model arrived at after training, validation, and evaluation.
Machine Learning Service (ML Service)
A ML Instance deployed as a Service to support on-demand requests for training and scoring using an API endpoint. An ML Service can also be created using existing trained Experiment Runs.

Create an ML Service with an existing training Experiment Run and scheduled scoring

When you publish a training Experiment Run as an ML Service, you can schedule scoring by providing details for the scoring Experiment Run the payload of a POST request. This results in the creation of a scheduled Experiment entity for scoring.
API format
POST /mlServices

Request
curl -X POST 
  https://platform.adobe.io/data/sensei/mlServices
  -H 'Authorization: {ACCESS_TOKEN}' 
  -H 'x-api-key: {API_KEY}' 
  -H 'x-gw-ims-org-id: {IMS_ORG}'
  -H 'Content-Type: application/json'
  -d '{
        "name": "Service name",
        "description": "Service description",
        "trainingExperimentId": "c4155146-b38f-4a8b-86d8-1de3838c8d87",
        "trainingExperimentRunId": "5c5af39c73fcec153117eed1",
        "scoringDataSetId": "5c5af39c73fcec153117eed1",
        "scoringTimeframe": "20000",
        "scoringSchedule": {
          "startTime": "2019-04-09T00:00",
          "endTime": "2019-04-10T00:00",
          "cron": "10 * * * *"
        }
      }'

Property
Description
mlInstanceId
Existing ML Instance identification, the training Experiment Run used to create the ML Service should correspond to this particular ML Instance.
trainingExperimentId
Experiment identification corresponding to the ML Instance identification.
trainingExperimentRunId
A particular training Experiment Run to be used for publishing the ML Service.
scoringDataSetId
Identification referring to the specific data set to be used for scheduled scoring Experiment Runs.
scoringTimeframe
An Integer value representing minutes for filtering data to be used for scoring Experiment Runs. For example, a value of 10080 means data from the past 10080 minutes or 168 hours will be used for each scheduled scoring Experiment Run. Note that a value of 0 will not filter data, all data within the dataset is used for scoring.
scoringSchedule
Contains details regarding scheduled scoring Experiment Runs.
scoringSchedule.startTime
Datetime indicating when to start scoring.
scoringSchedule.endTime
Datetime indicating when to start scoring.
scoringSchedule.cron
Cron value indicating the interval by which to score Experiment Runs.
Response
A successful response returns the details of the newly created ML Service, including its unique id and the scoringExperimentId for its corresponding scoring Experiment.
{
  "id": "string",
  "name": "string",
  "description": "string",
  "mlInstanceId": "string",
  "trainingExperimentId": "string",
  "trainingExperimentRunId": "string",
  "scoringExperimentId": "string",
  "scoringDataSetId": "string",
  "scoringTimeframe": "integer",
  "scoringSchedule": {
    "startTime": "2019-03-13T00:00",
    "endTime": "2019-03-14T00:00",
    "cron": "30 * * * *"
  },
  "created": "2019-04-08T14:45:25.981Z",
  "updated": "2019-04-08T14:45:25.981Z"
}

Creating an ML Service from an existing ML Instance

Depending on your specific use case and requirements, creating an ML Service with an ML Instance is flexible in terms of scheduling training and scoring Experiment Runs. This tutorial will go through the specific cases where:
Note that an ML Service can be created using an ML Instance without scheduling any training or scoring Experiments. Such ML Services will create ordinary Experiment entities and a single Experiment Run for training and scoring.

ML Service with scheduled Experiment for scoring

You can create an ML Service by publishing an ML Instance with scheduled Experiment Runs for scoring, which will create an ordinary Experiment entity for training. A training Experiment Run is generated and will be used for all scheduled scoring Experiment Runs. Ensure you have the mlInstanceId , trainingDataSetId , and scoringDataSetId required for the creation of the ML Service, and that they exist and are valid values.
API format
POST /mlServices

Request
curl -X POST 
  https://platform.adobe.io/data/sensei/mlServices
  -H 'Authorization: {ACCESS_TOKEN}' 
  -H 'x-api-key: {API_KEY}' 
  -H 'x-gw-ims-org-id: {IMS_ORG}' 
  -H 'x-sandbox-name: {SANDBOX_NAME}'
  -d '{
        "name": "Service name",
        "description": "Service description",
        "mlInstanceId": "c4155146-b38f-4a8b-86d8-1de3838c8d87",
        "trainingDataSetId": "5c5af39c73fcec153117eed1",
        "trainingTimeframe": "10000",
        "scoringDataSetId": "5c5af39c73fcec153117eed1",
        "scoringTimeframe": "20000",
        "scoringSchedule": {
          "startTime": "2019-04-09T00:00",
          "endTime": "2019-04-10T00:00",
          "cron": "10 * * * *"
        }
      }'

JSON key
Description
mlInstanceId
Existing ML Instance identification, representing the ML Instance used to create the ML Service.
trainingDataSetId
Identification referring to the specific data set to be used for training Experiment.
trainingTimeframe
An Integer value representing minutes for filtering data to be used for training Experiment. For example, a value of "10080" means data from the past 10080 minutes or 168 hours will be used for the training Experiment Run. Note that a value of "0" will not filter data, all data within the dataset is used for training.
scoringDataSetId
Identification referring to the specific data set to be used for scheduled scoring Experiment Runs.
scoringTimeframe
An Integer value representing minutes for filtering data to be used for scoring Experiment Runs. For example, a value of "10080" means data from the past 10080 minutes or 168 hours will be used for each scheduled scoring Experiment Run. Note that a value of "0" will not filter data, all data within the dataset is used for scoring.
scoringSchedule
Contains details regarding scheduled scoring Experiment Runs.
scoringSchedule.startTime
Datetime indicating when to start scoring.
scoringSchedule.endTime
Datetime indicating when to start scoring.
scoringSchedule.cron
Cron value indicating the interval by which to score Experiment Runs.
Response
A successful response returns the details of the newly created ML Service. This includes the service's unique id , as well as the trainingExperimentId and scoringExperimentId for its corresponding training and scoring Experiments, respectively.
{
  "id": "string",
  "name": "string",
  "description": "string",
  "mlInstanceId": "string",
  "trainingExperimentId": "string",
  "trainingDataSetId": "string",
  "trainingTimeframe": "integer",
  "scoringExperimentId": "string",
  "scoringDataSetId": "string",
  "scoringTimeframe": "integer",
  "scoringSchedule": {
    "startTime": "2019-04-09T00:00",
    "endTime": "2019-04-10T00:00",
    "cron": "10 * * * *"
  },
  "created": "2019-04-09T08:58:10.956Z",
  "updated": "2019-04-09T08:58:10.956Z"
}

ML Service with scheduled Experiments for training and scoring

To publish an existing ML Instance as an ML Service with scheduled training and scoring Experiment Runs, you are required to provide both training and scoring schedules. When an ML Service of this configuration is created, scheduled Experiment entities for both training and scoring is also created. Note that training and scoring schedules do not have to be the same. During a scoring job execution, the latest trained model produced by scheduled training Experiment Runs will be fetched and used for the scheduled scoring run.
API format
POST /mlServices

Request
curl -X POST 'https://platform-int.adobe.io/data/sensei/mlServices' 
  -H 'Authorization: Bearer {ACCESS_TOKEN}' 
  -H 'x-api-key: {API_KEY}' 
  -H 'x-gw-ims-org-id: {IMS_ORG}' 
  -H 'x-sandbox-name: {SANDBOX_NAME}'
  -d '{
        "name": "string",
        "description": "string",
        "mlInstanceId": "string",
        "trainingDataSetId": "string",
        "trainingTimeframe": "string",
        "scoringDataSetId": "string",
        "scoringTimeframe": "string",
        "trainingSchedule": {
          "startTime": "2019-04-09T00:00",
          "endTime": "2019-04-10T00:00",
          "cron": "10 * * * *"
        },
        "scoringSchedule": {
          "startTime": "2019-04-09T00:00",
          "endTime": "2019-04-10T00:00",
          "cron": "10 * * * *"
        }
      }'

JSON key
Description
mlInstanceId
Existing ML Instance identification, representing the ML Instance used to create the ML Service.
trainingDataSetId
Identification referring to the specific data set to be used for training Experiment.
trainingTimeframe
An Integer value representing minutes for filtering data to be used for training Experiment. For example, a value of "10080" means data from the past 10080 minutes or 168 hours will be used for the training Experiment Run. Note that a value of "0" will not filter data, all data within the dataset is used for training.
scoringDataSetId
Identification referring to the specific data set to be used for scheduled scoring Experiment Runs.
scoringTimeframe
An Integer value representing minutes for filtering data to be used for scoring Experiment Runs. For example, a value of "10080" means data from the past 10080 minutes or 168 hours will be used for each scheduled scoring Experiment Run. Note that a value of "0" will not filter data, all data within the dataset is used for scoring.
trainingSchedule
Contains details regarding scheduled training Experiment Runs.
scoringSchedule
Contains details regarding scheduled scoring Experiment Runs.
scoringSchedule.startTime
Datetime indicating when to start scoring.
scoringSchedule.endTime
Datetime indicating when to start scoring.
scoringSchedule.cron
Cron value indicating the interval by which to score Experiment Runs.
Response
A successful response returns the details of the newly created ML Service. This includes the service's unique id , as well as the trainingExperimentId and scoringExperimentId of its corresponding training and scoring Experiments, respectively. In the example response below, the presence of trainingSchedule and scoringSchedule suggests that the Experiment entities for training and scoring are scheduled Experiments.
{
  "id": "string",
  "name": "string",
  "description": "string",
  "mlInstanceId": "string",
  "trainingExperimentId": "string",
  "trainingDataSetId": "string",
  "trainingTimeframe": "integer",
  "scoringExperimentId": "string",
  "scoringDataSetId": "string",,
  "scoringTimeframe": "integer",
  "trainingSchedule": {
    "startTime": "2019-04-09T00:00",
    "endTime": "2019-04-10T00:00",
    "cron": "10 * * * *"
  },
  "scoringSchedule": {
    "startTime": "2019-04-09T00:00",
    "endTime": "2019-04-10T00:00",
    "cron": "10 * * * *"
  },
  "created": "2019-04-09T08:58:10.956Z",
  "updated": "2019-04-09T08:58:10.956Z"
}

Look up an ML Service

You can look up an existing ML Service by making a GET request to /mlServices and providing the unique id of the ML Service in the path.
API format
GET /mlServices/{SERVICE_ID}

Parameter
Description
{SERVICE_ID}
The unique id of the ML Service you are looking up.
Request
curl -X GET 'https://platform.adobe.io/data/sensei/mlServices/{SERVICE_ID}' 
  -H 'Authorization: Bearer {ACCESS_TOKEN}' 
  -H 'x-api-key: {API_KEY}' 
  -H 'x-gw-ims-org-id: {IMS_ORG}' 
  -H 'x-sandbox-name: {SANDBOX_NAME}'

Response
A successful response returns the details of the ML Service.
{
  "id": "string",
  "name": "string",
  "description": "string",
  "mlInstanceId": "string",
  "trainingExperimentId": "string",
  "trainingDataSetId": "string",
  "trainingTimeframe": "integer",
  "scoringExperimentId": "string",
  "scoringDataSetId": "string",
  "scoringTimeframe": "integer",
  "trainingSchedule": {
    "startTime": "2019-04-09T00:00",
    "endTime": "2019-04-10T00:00",
    "cron": "10 * * * *"
  },
  "scoringSchedule": {
    "startTime": "2019-04-09T00:00",
    "endTime": "2019-04-10T00:00",
    "cron": "10 * * * *"
  },
  "created": "2019-05-13T23:46:03.478Z",
  "updated": "2019-05-13T23:46:03.478Z"
}

Retrieving different ML Services may return a response with more or less key-value pairs. The above response is a representation of a ML Service with both scheduled training and scoring Experiment Runs .

Schedule training or scoring

If you want to schedule scoring and training on an ML Service that has already been published, you can do so by updating the existing ML Service with a PUT request on /mlServices .
API format
PUT /mlServices/{SERVICE_ID}

Parameter
Description
{SERVICE_ID}
The unique id of the ML Service you are updating.
Request
The following request schedules training and scoring for an existing ML Service by adding the trainingSchedule and scoringSchedule keys with their respective startTime , endTime , and cron keys.
curl -X PUT 'https://platform.adobe.io/data/sensei/mlServices/{SERVICE_ID}' 
  -H 'Authorization: {ACCESS_TOKEN}' 
  -H 'x-api-key: {API_KEY}' 
  -H 'x-gw-ims-org-id: {IMS_ORG}' 
  -H 'x-sandbox-name: {SANDBOX_NAME}'
  -d '{
        "name": "string",
        "description": "string",
        "mlInstanceId": "string",
        "trainingExperimentId": "string",
        "trainingDataSetId": "string",
        "trainingTimeframe": "integer",
        "scoringExperimentId": "string",
        "scoringDataSetId": "string",
        "scoringTimeframe": "integer",
        "trainingSchedule": {
          "startTime": "2019-04-09T00:00",
          "endTime": "2019-04-11T00:00",
          "cron": "20 * * * *"
        },
        "scoringSchedule": {
          "startTime": "2019-04-09T00:00",
          "endTime": "2019-04-11T00:00",
          "cron": "20 * * * *"
        }
      }'

Do not attempt to modify the startTime on existing scheduled training and scoring jobs. If the startTime must be modified, consider publishing the same Model and rescheduling training and scoring jobs.
Response
A successful response returns the details of the updated ML Service.
{
  "id": "string",
  "name": "string",
  "description": "string",
  "mlInstanceId": "string",
  "trainingExperimentId": "string",
  "trainingDataSetId": "string",
  "trainingTimeframe": "integer",
  "scoringExperimentId": "string",
  "scoringDataSetId": "string",
  "scoringTimeframe": "integer",
  "trainingSchedule": {
    "startTime": "2019-04-09T00:00",
    "endTime": "2019-04-11T00:00",
    "cron": "20 * * * *"
  },
  "scoringSchedule": {
    "startTime": "2019-04-09T00:00",
    "endTime": "2019-04-11T00:00",
    "cron": "20 * * * *"
  },
  "created": "2019-04-09T08:58:10.956Z",
  "updated": "2019-04-09T09:43:55.563Z"
}