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SMO - Model Catalog

The Model Catalog provides a list of Applications that are onboarded to the SMO after delivery of an "Application Package" from a vendor. It presents a Model-View-Controller for management of the data.

For the purposes of this implementation we will assume the following:

  • The Controller will be RESTful from the service endpoint of {apiRoot}/ModelCatalog/v1. The initial REST resource tree is show below. Additional resources may be added to represent aspect of ML Applications which have additional data elements.

 Click here to view PlantUML...

@startsalt
{
scale 1.5
{T
+…/modelCatalog/v1
++/applications
+++/<applicationVersionID>
++++/applicationServiceModels
+++++/<serviceModelID>
++++++/serviceResources
+++++++/<resourceID>
++++++++/descriptor
++++++++/instanceDeploymentVariables
++++++++/instanceApplicationConfigurationSchema
++++++++/policyTypes
+++++++++/<PolicyTypeID>
++++++++/dataFeeds
+++++++++/<dataFeedID>
++++++++/publishes
+++++++++/<publicationID>
++++++++/requiredImages
++/images
+++/<imageID>
}
}
@endsalt

  • The Model is represented in the class diagram below

 Click here to view plantUML

@startuml

Class Application {
   applicationVersionID : string
   vendor : string
   applicationName : string
   version : string
   serviceModels : ServiceModel[]
   state : string
   revisionID : string
}

Class ServiceModel {
   serviceModelID : string
   modelName : string
   modelDescroption : string
   resources : ServiceResource[]
}

Class ServiceResource {
   resourceID : string
   resourceType : string
   resourceVendor : string
   resourceName : string
   resourceVersion : string
   deploymentDescriptor : DeploymentDescriptor
   descriptorVariableOverrides : Tuple[]
   applicationConfigurationSchema : string
   policyTypes : PolicyType[]
   dataFeeds : DataFeed[]
   publications : Publications[]
   requiredImages : ImageID[]
}

Class Tuple {
   key : string
   value : string
}

Class DataFeed {
   sharedDataName : string
   mandatory : boolean
}

Class Publication {
   sharedDataName : string
   mandatory : boolean
}

Class PolicyType {
   policySchema : string
   policyStatusSchema : string
}

Class DeploymentDescriptor {
   descriptorType : string
   descriptor : string
}

Application "1" *-- "1..*" ServiceModel
ServiceModel "1" *-- "1..*" ServiceResource
ServiceResource "1" *-down- "1" DeploymentDescriptor
ServiceResource "1" *-down- "0..*" Tuple
ServiceResource "1" *-down- "0..*" PolicyType
ServiceResource "1" *-down- "0..*" DataFeed
ServiceResource "1" *-down- "0..*" Publication

@enduml


  • The View will be JSON.


The Application record in the Model Catalog follows a Stateful lifecycle. The State can be updated with a partial update (PUT) as long as the current revisionID is supplied as a query parameter. Upon a successful update the revisionID will be changed by the system to a newly generated value.


The valid values for State are "VALIDATED", "TRAINING_REQUIRED", and "AVAILABLE". The state transitions allowed are:

 Click here to view plantUML...

@startuml
[*] -> VALIDATED
VALIDATED : The validated package is sequestered into the catalog. This protects the process from a
VALIDATED :  non-validated package from being supplied to the cataloging step that is different from the
VALIDATED :  one provided on the validation step.

VALIDATED -down-> TRAINING_REQUIRED : ML detected
VALIDATED -> AVAILABLE : No ML
TRAINING_REQUIRED -> AVAILABLE : Training Complete
AVAILABLE -> TRAINING_REQUIRED : Retraining Required

TRAINING_REQUIRED : Training Iteration is tracked. It is initialized with a zero at validation.
TRAINING_REQUIRED : During training multiple training iterations may be applied.
TRAINING_REQUIRED : Therefore the count returned from training may increment by
TRAINING_REQUIRED : more than one. How we manage the availablity for different
TRAINING_REQUIRED : trained instances is FFS. For now only the latest trained
TRAINING_REQUIRED : version is "AVAILABLE".

AVAILABLE : This is an application in which the Run-Time can create a configuration for.
AVAILABLE : However, we may need to consider training iteration count as after a
AVAILABLE : configuration is created, additional training might become required
AVAILABLE : a determination needs to occur as FFS if we invalidate existing configuration
AVAILABLE : which raises a question on "RUNNING" instances. Or do we allow the
AVAILABLE : deployment of any iteration that reaches the AVAILABLE state.

@enduml

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