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Hitachi Vantara Lumada and Pentaho Documentation

Use machine learning service with IIoT Core

The Machine learning service is a component of Solution Management that enables you to train specific ML models by accessing historic data and routing datasets through it to influence responses.

The Machine learning service can optionally call third-party simulation engines, sending payloads and receiving back the result, synchronously. This creates the ability to augment existing data with inferred values that can’t be easily measured in the physical world, such as angular acceleration, torque, and so on.

Solution Management is where you train, track, manage, deploy, and monitor ML models.

Learn more

For help creating projects and building, retraining, deploying, and deleting ML models, contact your Hitachi Vantara representative.

Log into the Solution Management UI

To access the ML Model Manager application, log into the Solution Management UI.

Procedure

  1. From the command line on the installation node, get the username and password for the Solution Management UI:

    • Username:
      echo $(kubectl get keycloakusers -n hiota keycloak-user -o jsonpath='{.spec.user.username}')
    • Password:
      echo $(kubectl get keycloakusers -n hiota keycloak-user -o jsonpath='{.spec.user.credentials[0].value}')
  2. Log into the Solution Management UI using the acquired credentials:

    https://<cluster-fqdn>:30443/hiota/hscp-hiota/solution-control-plane/
    where <cluster-fqdn> is the location where IIoT Core Services is installed.

Results

The Solution Management console opens.

How to manage a model

Use the following actions to manage your Machine learning service model:

View ML service projects

Perform the following steps to view your projects:

Procedure

  1. Open the Lumada ML Model Management application.

  2. Select the Projects menu option at the top of the page.

Results

The Projects page displays a list of available projects with information about each one.
FieldDescription
NameName of the ML service project
StatusStatus of the ML service project
  • Draft

    The ML service project has no associated models with the status of Published.

  • Published

    The ML service project has at least one associated model with the status of Published.

TagsKeywords that describe the ML service project
DescriptionPurpose of the ML service project
CreatedDate the ML service project was created
Created ByUser who created the ML service project
ModifiedDate the ML service project was modified
Modified ByUser who modified the ML service project

View project details

You can view project details such as properties and deployment on the Projects page using the following steps:

Procedure

  1. Open the Lumada ML Model Management application.

  2. Select Projects from the menu at the top of the page.

  3. Select View Details in the Actions menu.

    The PROJECT PROPERTIES section contains information on the following fields:
    FieldDescription
    DescriptionPurpose of the project
    StatusStatus of the project
    • Draft

      The project has no associated models with the status of Published.

    • Published

      The project has at least one associated model with the status of Published.

    TagsKeywords that describe the project
    CreatedDate the project was created
    Created ByUser who created the project
    ModifiedDate the project was modified
    Modified ByUser who modified the project

    The DEPLOYMENT section contains information on the following fields of the ML model deployed to the project:

    FieldDescription
    NameName of the deployment
    Model NameName of the machine learning model
    Model VersionVersion of the model
    EndpointModel endpoint where the inferencing applications can integrate with the REST endpoint
    ASCCategory name of the analytic. For example, failure prediction
    Inferences/last hourTime when last inferencing occurred.
    Total InferencesTotal number of inferences occurred.

View models

Perform the following steps to view the models in your project:

Procedure

  1. Open the Lumada ML Model Management application.

  2. Select View Models in the Actions menu.

    The MODELS LIST displays with the following fields:

    FieldDescription
    NameName of the machine learning model
    Status

    Status of the ML model

    • Draft

      A model that has been created but does not have a version. It is an empty model.

    • Ready

      A model that has at least one version with trained. status

    • Published

      A model that has at least one version with deployed status.

    TagsKeywords that describe the ML model
    ASCCategory name of the analytic. For example, Failure Prediction
    CreatedDate the ML model was created
    Created ByUser who created the ML model
    ModifiedDate the ML model was modified
    Modified ByUser who modified the ML model

View model repository

Perform the following steps to view the model repository:

Procedure

  1. Open the Lumada ML Model Management application.

  2. Select Model Repository from the menu at the top of the page.

    The list of models in the project will display with the following fields:

    FieldDescription
    NameName of the ML model
    StatusStatus of the ML model
    • Draft

      A model has been created but has no version. It is an empty model.

    • Ready

      A model has at least one version with trained status.

    • Published

      A model has at least one version with Published status.

    ProjectProject to which this ML model belongs.
    TagsKeywords that describe the ML models
    ASCCategory name of the analytics. For example, Failure Prediction.
    CreatedDate the ML model was created.
    Created ByUser who created the ML model.
    ModifiedDate the ML model was modified.
    Modified ByUser who modified the ML model.

View model details

Perform the following steps to view the properties of a model:

Procedure

  1. Open the Lumada ML Model Management application.

  2. At the top of the page, select Model Repository.

  3. Select View Details from the Actions menu next to the model on which you want to view the details.

    The following properties of the model you selected are displayed:

    FieldDescription
    DescriptionDescription of the ML model
    StatusStatus of the ML model
    • Draft

      A model has been created but has no version. It is an empty model.

    • Ready

      A model has at least one version with trained status.

    • Published

      A model has at least one version with Published status.

    TagsKeywords that describe the ML models
    ProjectProject to which this ML model belongs.
    ASCCategory name of the analytics. For example, Failure Prediction.
    CreatedDate the ML model was created.
    Created ByUser who created the ML model.
    ModifiedDate the ML model was modified.
    Modified ByUser who modified the ML model.

View model versions

Perform the followings steps to view the versions of the machine learning models:

Procedure

  1. Open the Lumada ML Model Management application.

  2. At the top of the page, select Model Repository.

  3. Select View Versions from the Actions menu next to the model on which you want to view the details.

    The following properties of the model versions are displayed:

    FieldDescription
    NameVersion name
    Status

    Status of each version. The status values are:

    • Trained

      A version has been created or trained but has not been deployed.

    • Deployed

      The version is deployed.

    DatasetsThe datasets used for the model training. For example, the location where the dataset is retrieved. This field will vary depending on how the ML model is built.
    MetricsPerformance metrics of the version. This field will vary depending on how the ML model is built.
    ParametersThe parameters of the model version. This field will vary depending on how the ML model is built.
    TrainingThe training duration of each version

Compare model versions

Perform the following steps to compare two versions of a model:

Procedure

  1. Open the Lumada ML Model Management application.

  2. At the top of the page, select Model Repository.

  3. Select View Versions in the Actions menu.

  4. Select the checkbox for the two versions that you want to compare and click Compare .

Results

A summary of the datasets, parameters, and metrics is displayed. A metrics graphic comparison is also displayed.