The service of the Auto ML platform is responsable for the training of models as well as the determination of predictions based on those models.

Characteristics

Function

The Machine Learning (ML) Pipeline is the heart of the Auto ML Platform and as such responsible for data ingestion, data validation, transformation, machine learning training, model evaluation, and model serving. The pipeline is based on MLFlow, but other providers, such as Google TFX, Kubeflow, etc. can be used alternatively.

Requests for the ML Pipeline are managed via separate services that provide an own API:

• The PREDICT-API service provides prediction and status endpoints that can be called by client applications.
  >> PREDICT-API Endpoints

The management of the ML Pipeline is done via the command line application Kairos CLI.

Machine Learning Training

The ML Pipeline needs to be trained by means of reference objects stored in a document management system, e.g., yuuvis® Momentum, and for which users manually defined the individual object type. The data exported from yuuvis® Momentum is stored in the format suitable for data ingestion on local storage or S3. This data is used to train the models for the determination of predictions.

Document Classification

In the context of the AI platform, classification means the determination of suitable typification classes fitting for an object based on its full-text rendition. For one object, one prediction is provided that contains mappings of classes and their corresponding relevance probability as well as a reference on the object in yuuvis® Momentum via objectId.

Instead of the class names used internally in the ML Pipeline, the prediction response bodies provide the object types as referenced in the inference schema described below.

Metadata Extraction

ML Pipeline can analyze the PDF rendition of binary content files assigned to objects in yuuvis® Momentum in order to extract specific metadata. Based on the trained models, predictions for values of specific object properties can be determined. The object properties have to be listed in the inference schema where also conditions for the values and settings for the prediction responses are specified.

Inference Schema

The inference schema is a JSON configuration file defining the object types that will be available for the classification as well as the properties for which the metadata extraction should determine suitable values. At the same time, each internal aiObjectTypeId (aiPropertyId) gets a corresponding objectTypeId (propertyId) that will be used in the response bodies of the classification (extraction) endpoints to be compatible with the connected client application.

The inference schema is defined for a specific tenant. It is also possible to further limit the usage of the inference schema to an app by specifying appName (e.g., to distinct between a client app for single uploads and batch processing apps).

{
    "tenant" : "mytenant",
    "appName" : "AIInvoiceClient",
    "classification" : {
        "enabled" : true,
        "timeout" : 2,
        "aiClassifierId" : "DOCUMENT_CLASSIFICATION",
        "objectTypes": [
            {
                "objectTypeId" : "appImpulse:receiptsot|appImpulse:receiptType|Rechnung",
                "aiObjectTypeId" : "INVOICE"
            },
            {
                "objectTypeId" : "appImpulse:receiptsot|appImpulse:receiptType|Angebot",
                "aiObjectTypeId" : "DOCUMENT_TYPE_2"
            },
            {
                "objectTypeId" : "appImpulse:hrsot|appImpulse:receiptType|Bewerbung",
                "aiObjectTypeId" : "DOCUMENT_TYPE_3"
            }
        ]
    },
    "extraction" : {
        "enabled" : true,
        "timeout" : 5,
        "objects" : [
            {
                "objectTypeId" : "invoice",
                "enabled" : true,
                "timeout" : 10,
                "propertyReference" : [
                    {
                        "propertyId" : "companyName",
                        "aiPropertyId" : "INVOICE_COMPANY_NAME",
                        "allowedValues" : ["Company1", "Company2", "Company3"],
                        "pattern" : "/^[a-z]|\\d?[a-zA-Z0-9]?[a-zA-Z0-9\\s&@.]+$",
                        "validationService" : "my_company_name_validation_service",
                        "maxNumberOfPredictions" : 5
                    },
                    {
                        "propertyId" : "totalAmount",
                        "aiPropertyId" : "INVOICE_TOTAL_AMOUNT",
                        "pattern" : "^[0-9]*[.][0-9]*$",
                        "validationService" : "my_amounts_validation_service",
                        "maxNumberOfPredictions" : 1
                    }
                ]
            }
        ]
    }
}

The following parameters are available in the inference schema:

In order to combine the AI platform with yuuvis® client as reference implementation, the following inference schema is required:

{
    "tenant" : "os__papi",
    "appName" : "AIInvoiceClient",
    "classification" : {
        "enabled" : true,
        "timeout" : 10,
        "aiClassifierId" : "DOCUMENT_CLASSIFICATION",
		"objectTypes": [ 
			{ 
				"objectTypeId" : "appImpulse:hrdocsot|appImpulse:hrDocumentType|Bescheinigung",
				"aiObjectTypeId" : "appImpulse:contractsot"
			},
			{ 
				"objectTypeId" : "appImpulse:receiptsot",
				"aiObjectTypeId" : "appImpulse:hrdocsot"
			},
			{ 
				"objectTypeId" : "appImpulse:contractsot|appImpulse:contractType|Arbeitsvertrag",
				"aiObjectTypeId" : "appImpulse:receiptsot"
			},
			{ 
				"objectTypeId" : "appImpulse:hrdocsot|appImpulse:hrDocumentType|Arbeitsvertrag",
				"aiObjectTypeId" : "appImpulse:basedocumentsot"
			}
		]
    }
}

Requirements

The Auto ML Pipeline is a part of the Auto ML Platform and can run only in combination with the other included services.

ML Pipeline furthermore requires:

• s3 or local storage

If you want to use the ML Pipeline for the AI integration in yuuvis® client as reference implementation, also the requirements of the CLIENT Service have to be considered.

Installation

The Auto ML Platform services including the ML Pipeline are not yet included in yuuvis® Momentum installations but only available on request.

Configuration

The ML Pipeline is managed, configured and maintained via the command line application Kairos CLI.