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.
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
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}')
- Username:
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
Use Model Management
Use the following actions to manage your Machine learning service models:
- View ML service projects
- View project details
- View models
- View model repository
- View model details
- View model versions
- Compare model versions
View ML service projects
Procedure
Open the Lumada ML Model Manager application.
Select the Projects menu option at the top of the page.
Results
| Field | Description |
| Name | Name of the ML service project |
| Status | Status of the ML service project
|
| Tags | Keywords that describe the ML service project |
| Description | Purpose of the ML service project |
| Created | Date the ML service project was created |
| Created By | User who created the ML service project |
| Modified | Date the ML service project was modified |
| Modified By | User 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
Open the Lumada ML Model Manager application.
Select Projects from the menu at the top of the page.
Select the project for which you want to view the details.
Select View Details in the Actions menu.
The PROJECT PROPERTIES section contains information on the following fields:Field Description Description Purpose of the project Status Status 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.
Tags Keywords that describe the project Created Date the project was created Created By User who created the project Modified Date the project was modified Modified By User who modified the project The DEPLOYMENT section contains information on the following fields of the ML model deployed to the project:
NoteYou may have to scroll down the Project details page to view the DEPLOYMENT section.Field Description Name Name of the deployment Model Name Name of the machine learning model Model Version Version of the model Endpoint Model endpoint where the inferencing applications can integrate with the REST endpoint ASC Category name of the analytic. For example, failure prediction Inferences/last hour Time when last inferencing occurred. Total Inferences Total number of inferences occurred.
View models
Procedure
Open the Lumada ML Model Manager application.
Click Model Repository from the menu at the top of the page.
Select the model for which you want the details.
The MODELS LIST displays with the following fields:
Field Description Name Name of the machine learning model Description User-defined description of the 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.
Tags Keywords that describe the ML model Project The project to which the model belongs. ASC Category name of the analytic. For example, Failure Prediction. Created Date the ML model was created. Created By User who created the ML model. Modified Date the ML model was modified. Modified By User who modified the ML model.
View model repository
Procedure
Open the Lumada ML Model Manager application.
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:
Field Description Name Name of the ML model Status Status 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.
Project Project to which this ML model belongs. Tags Keywords that describe the ML models ASC Category name of the analytics. For example, Failure Prediction. Created Date the ML model was created. Created By User who created the ML model. Modified Date the ML model was modified. Modified By User who modified the ML model.
View model details
Procedure
Open the Lumada ML Model Manager application.
Select Model Repository from the menu at the top of the page.
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:
Field Description Description Description of the ML model Status Status 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.
Tags Keywords that describe the ML models Project Project to which this ML model belongs. ASC Category name of the analytics. For example, Failure Prediction. Created Date the ML model was created. Created By User who created the ML model. Modified Date the ML model was modified. Modified By User who modified the ML model.
View model versions
Procedure
Open the Lumada ML Model Manager application.
Select Model Repository from the menu at the top of the page.
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:
Field Description Name Version name Status Status of each version. The status values are: Trained
A version has been created or trained but has not been deployed.
Deployment pending
Model is In the process of being deployed, pending resource availability.
Deployed
The version is deployed.
Datasets The 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. Metrics Performance metrics of the version. This field will vary depending on how the ML model is built. Parameters The parameters of the model version. This field will vary depending on how the ML model is built. Training The training duration of each version
Compare model versions
Procedure
Open the Lumada ML Model Manager application.
Select Model Repository from the menu at the top of the page.
Select your project from the Filter by project menu.
Select View Versions in the Actions menu.
Select the checkbox for the two versions that you want to compare and click Compare.
Results