Skip to content

ML Model Service

MLOps platforms and model registries - managing machine learning models at scale

Overview

The ML Model Service entity represents MLOps platforms and model registries that track, manage, and serve machine learning models. It is the top-level container for ML models and connects to platforms like MLflow, SageMaker, Vertex AI, and other ML lifecycle management tools.

Hierarchy: 

graph LR
    A[MLModelService] --> B[MLModel]
    B --> C[Features]

    style A fill:#667eea,color:#fff
    style B fill:#4facfe,color:#fff
    style C fill:#00f2fe,color:#333

Relationships

MLModelService has comprehensive relationships with entities across the metadata platform:

graph TD
    subgraph Service Layer
        A[MLModelService<br/>mlflow_prod]
    end

    subgraph ML Models
        A --> B1[MLModel:<br/>churn_predictor]
        A --> B2[MLModel:<br/>fraud_detection]
        A --> B3[MLModel:<br/>recommendation_engine]

        B1 --> C1[Feature:<br/>recency]
        B1 --> C2[Feature:<br/>frequency]
        B1 --> C3[Feature:<br/>monetary]

        B2 --> C4[Feature:<br/>transaction_amount]
        B2 --> C5[Feature:<br/>merchant_category]

        B3 --> C6[Feature:<br/>user_preferences]
        B3 --> C7[Feature:<br/>product_similarity]
    end

    subgraph Ownership
        A -.->|owned by| D1[Team:<br/>Data Science]
        A -.->|owned by| D2[User:<br/>ml.engineer]
    end

    subgraph Governance
        A -.->|in domain| E[Domain:<br/>ML Platform]
        A -.->|has tags| F1[Tag:<br/>Production]
        A -.->|has tags| F2[Tag:<br/>Tier.Gold]
        A -.->|has tags| F3[Tag:<br/>Sensitive]
    end

    subgraph Training Data Lineage
        G1[Table:<br/>customer_features] -.->|trains| B1
        G2[Table:<br/>transaction_history] -.->|trains| B2
        G3[Table:<br/>user_interactions] -.->|trains| B3

        B1 -.->|predictions to| H1[Table:<br/>churn_scores]
        B2 -.->|predictions to| H2[Table:<br/>fraud_scores]
        B3 -.->|predictions to| H3[Table:<br/>recommendations]
    end

    subgraph ML Pipelines
        I1[Pipeline:<br/>feature_engineering] -.->|creates features for| B1
        I2[Pipeline:<br/>model_training] -.->|trains| B1
        I3[Pipeline:<br/>batch_inference] -.->|runs predictions| B1

        J1[Pipeline:<br/>fraud_feature_pipeline] -.->|creates features for| B2
        J2[Pipeline:<br/>fraud_training_pipeline] -.->|trains| B2
    end

    subgraph Model Versions & Metrics
        B1 -.->|has metrics| K1[Accuracy: 92%<br/>AUC: 0.93]
        B2 -.->|has metrics| K2[Precision: 0.85<br/>Recall: 0.88]
        B3 -.->|has metrics| K3[NDCG: 0.78<br/>MAP: 0.72]
    end

    style A fill:#667eea,color:#fff,stroke:#4c51bf,stroke-width:3px
    style B1 fill:#4facfe,color:#fff
    style B2 fill:#4facfe,color:#fff
    style B3 fill:#4facfe,color:#fff
    style C1 fill:#00f2fe,color:#333
    style C2 fill:#00f2fe,color:#333
    style C3 fill:#00f2fe,color:#333
    style C4 fill:#00f2fe,color:#333
    style C5 fill:#00f2fe,color:#333
    style C6 fill:#00f2fe,color:#333
    style C7 fill:#00f2fe,color:#333
    style D1 fill:#43e97b,color:#fff
    style D2 fill:#43e97b,color:#fff
    style E fill:#fa709a,color:#fff
    style F1 fill:#f093fb,color:#fff
    style F2 fill:#f093fb,color:#fff
    style F3 fill:#f093fb,color:#fff
    style G1 fill:#764ba2,color:#fff
    style G2 fill:#764ba2,color:#fff
    style G3 fill:#764ba2,color:#fff
    style H1 fill:#f5576c,color:#fff
    style H2 fill:#f5576c,color:#fff
    style H3 fill:#f5576c,color:#fff
    style I1 fill:#00ac69,color:#fff
    style I2 fill:#00ac69,color:#fff
    style I3 fill:#00ac69,color:#fff
    style J1 fill:#00ac69,color:#fff
    style J2 fill:#00ac69,color:#fff
    style K1 fill:#ffd700,color:#333
    style K2 fill:#ffd700,color:#333
    style K3 fill:#ffd700,color:#333

Relationship Types:

  • Solid lines (→): Hierarchical containment (Service manages Models, Models have Features)
  • Dashed lines (-.->): References and associations (ownership, governance, lineage, training, inference)

Child Entities

  • MlModel: ML models managed by this service

Associated Entities

  • Owner: User or team owning this service
  • Domain: Business domain assignment
  • Tag: Classification tags
  • Table: Tables for training data and predictions (via lineage)
  • Pipeline: Feature engineering, training, and inference pipelines

Schema Specifications

View the complete ML Model Service schema in your preferred format:

Complete JSON Schema Definition

{
  "$id": "https://open-metadata.org/schema/entity/services/mlmodelService.json",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "title": "MLModelService",
  "description": "An `MLModelService` represents an MLOps platform or model registry that manages machine learning models.",
  "type": "object",
  "javaType": "org.openmetadata.schema.entity.services.MlModelService",

  "definitions": {
    "mlModelServiceType": {
      "description": "Type of ML Model service",
      "type": "string",
      "enum": [
        "MLflow", "SageMaker", "VertexAI", "AzureML",
        "Databricks", "Kubeflow", "CustomML", "WandB",
        "Neptune", "H2O", "HuggingFace"
      ]
    },
    "mlModelConnection": {
      "type": "object",
      "properties": {
        "config": {
          "oneOf": [
            {"$ref": "#/definitions/mlflowConnection"},
            {"$ref": "#/definitions/sagemakerConnection"},
            {"$ref": "#/definitions/vertexAIConnection"}
          ]
        }
      }
    },
    "mlflowConnection": {
      "type": "object",
      "properties": {
        "type": {"const": "MLflow"},
        "trackingUri": {
          "type": "string",
          "description": "MLflow tracking server URI"
        },
        "registryUri": {
          "type": "string",
          "description": "MLflow model registry URI"
        }
      },
      "required": ["type", "trackingUri"]
    },
    "sagemakerConnection": {
      "type": "object",
      "properties": {
        "type": {"const": "SageMaker"},
        "awsRegion": {
          "type": "string",
          "description": "AWS region"
        },
        "awsAccessKeyId": {
          "type": "string",
          "description": "AWS access key"
        },
        "awsSecretAccessKey": {
          "type": "string",
          "description": "AWS secret key"
        }
      },
      "required": ["type", "awsRegion"]
    },
    "vertexAIConnection": {
      "type": "object",
      "properties": {
        "type": {"const": "VertexAI"},
        "gcpProjectId": {
          "type": "string",
          "description": "GCP project ID"
        },
        "gcpRegion": {
          "type": "string",
          "description": "GCP region"
        }
      },
      "required": ["type", "gcpProjectId"]
    }
  },

  "properties": {
    "id": {
      "description": "Unique identifier",
      "$ref": "../../type/basic.json#/definitions/uuid"
    },
    "name": {
      "description": "Service name",
      "$ref": "../../type/basic.json#/definitions/entityName"
    },
    "fullyQualifiedName": {
      "description": "Fully qualified name (same as name for services)",
      "$ref": "../../type/basic.json#/definitions/fullyQualifiedEntityName"
    },
    "displayName": {
      "description": "Display name",
      "type": "string"
    },
    "description": {
      "description": "Markdown description",
      "$ref": "../../type/basic.json#/definitions/markdown"
    },
    "serviceType": {
      "$ref": "#/definitions/mlModelServiceType"
    },
    "connection": {
      "description": "Connection configuration",
      "$ref": "#/definitions/mlModelConnection"
    },
    "owner": {
      "description": "Owner (user or team)",
      "$ref": "../../type/entityReference.json"
    },
    "domain": {
      "description": "Data domain",
      "$ref": "../../type/entityReference.json"
    },
    "tags": {
      "description": "Classification tags",
      "type": "array",
      "items": {
        "$ref": "../../type/tagLabel.json"
      }
    },
    "version": {
      "description": "Metadata version",
      "$ref": "../../type/entityHistory.json#/definitions/entityVersion"
    }
  },

  "required": ["id", "name", "serviceType", "connection"]
}

View Full JSON Schema →

RDF/OWL Ontology Definition

@prefix om: <https://open-metadata.org/schema/> .
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
@prefix owl: <http://www.w3.org/2001/XMLSchema#> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .

# MLModelService Class Definition
om:MLModelService a owl:Class ;
    rdfs:subClassOf om:Service ;
    rdfs:label "ML Model Service" ;
    rdfs:comment "An MLOps platform or model registry managing machine learning models" ;
    om:hierarchyLevel 1 .

# Properties
om:serviceName a owl:DatatypeProperty ;
    rdfs:domain om:MLModelService ;
    rdfs:range xsd:string ;
    rdfs:label "name" ;
    rdfs:comment "Name of the ML model service" .

om:serviceType a owl:DatatypeProperty ;
    rdfs:domain om:MLModelService ;
    rdfs:range om:MLModelServiceType ;
    rdfs:label "serviceType" ;
    rdfs:comment "Type of ML model service (MLflow, SageMaker, etc.)" .

om:trackingUri a owl:DatatypeProperty ;
    rdfs:domain om:MLModelService ;
    rdfs:range xsd:anyURI ;
    rdfs:label "trackingUri" ;
    rdfs:comment "URI for the ML tracking server" .

om:hasMlModel a owl:ObjectProperty ;
    rdfs:domain om:MLModelService ;
    rdfs:range om:MlModel ;
    rdfs:label "hasMlModel" ;
    rdfs:comment "Models managed by this service" .

om:ownedBy a owl:ObjectProperty ;
    rdfs:domain om:MLModelService ;
    rdfs:range om:Owner ;
    rdfs:label "ownedBy" ;
    rdfs:comment "User or team that owns this service" .

om:hasTag a owl:ObjectProperty ;
    rdfs:domain om:MLModelService ;
    rdfs:range om:Tag ;
    rdfs:label "hasTag" ;
    rdfs:comment "Classification tags applied to service" .

# Service Type Enumeration
om:MLModelServiceType a owl:Class ;
    owl:oneOf (
        om:MLflowService
        om:SageMakerService
        om:VertexAIService
        om:AzureMLService
        om:DatabricksMLService
    ) .

# Example Instance
ex:mlflowProduction a om:MLModelService ;
    om:serviceName "mlflow_prod" ;
    om:fullyQualifiedName "mlflow_prod" ;
    om:serviceType om:MLflowService ;
    om:trackingUri "https://mlflow.company.com" ;
    om:ownedBy ex:dataScience ;
    om:hasTag ex:tierProduction ;
    om:hasMlModel ex:churnPredictor ;
    om:hasMlModel ex:fraudDetection .

View Full RDF Ontology →

JSON-LD Context and Example

{
  "@context": {
    "@vocab": "https://open-metadata.org/schema/",
    "om": "https://open-metadata.org/schema/",
    "rdfs": "http://www.w3.org/2000/01/rdf-schema#",
    "xsd": "http://www.w3.org/2001/XMLSchema#",

    "MLModelService": "om:MLModelService",
    "name": {
      "@id": "om:serviceName",
      "@type": "xsd:string"
    },
    "fullyQualifiedName": {
      "@id": "om:fullyQualifiedName",
      "@type": "xsd:string"
    },
    "displayName": {
      "@id": "om:displayName",
      "@type": "xsd:string"
    },
    "description": {
      "@id": "om:description",
      "@type": "xsd:string"
    },
    "serviceType": {
      "@id": "om:serviceType",
      "@type": "@vocab"
    },
    "connection": {
      "@id": "om:hasConnection",
      "@type": "@id"
    },
    "owner": {
      "@id": "om:ownedBy",
      "@type": "@id"
    },
    "domain": {
      "@id": "om:inDomain",
      "@type": "@id"
    },
    "tags": {
      "@id": "om:hasTag",
      "@type": "@id",
      "@container": "@set"
    }
  }
}

Example JSON-LD Instance:

{
  "@context": "https://open-metadata.org/context/mlmodelService.jsonld",
  "@type": "MLModelService",
  "@id": "https://example.com/services/mlflow_prod",

  "name": "mlflow_prod",
  "fullyQualifiedName": "mlflow_prod",
  "displayName": "MLflow Production",
  "description": "Production MLflow instance for model tracking and registry",
  "serviceType": "MLflow",

  "connection": {
    "config": {
      "type": "MLflow",
      "trackingUri": "https://mlflow.company.com",
      "registryUri": "https://mlflow.company.com"
    }
  },

  "owner": {
    "@id": "https://example.com/teams/data-science",
    "@type": "Team",
    "name": "data-science",
    "displayName": "Data Science Team"
  },

  "domain": {
    "@id": "https://example.com/domains/AI-ML",
    "@type": "Domain",
    "name": "AI-ML"
  },

  "tags": [
    {
      "@id": "https://open-metadata.org/tags/Tier/Production",
      "tagFQN": "Tier.Production"
    },
    {
      "@id": "https://open-metadata.org/tags/Environment/Prod",
      "tagFQN": "Environment.Prod"
    }
  ]
}

View Full JSON-LD Context →

Use Cases

  • Connect to MLflow, SageMaker, Vertex AI, and other ML platforms
  • Discover and catalog ML models across different registries
  • Track model ownership and governance by ML platform
  • Apply environment tags (production, staging, development)
  • Organize models by team and domain
  • Monitor model deployment across platforms
  • Ensure compliance for AI/ML systems
  • Enable cross-platform model lineage

JSON Schema Specification

Core Properties

id (uuid)

Type: string (UUID format) Required: Yes (system-generated) Description: Unique identifier for this ML model service instance

{
  "id": "7a8b9c0d-1e2f-3a4b-5c6d-7e8f9a0b1c2d"
}

name (entityName)

Type: string Required: Yes Pattern: ^[^.]*$ (no dots allowed) Min Length: 1 Max Length: 256 Description: Name of the ML model service

{
  "name": "mlflow_prod"
}

fullyQualifiedName (fullyQualifiedEntityName)

Type: string Required: Yes (system-generated) Description: For services, this is the same as name

{
  "fullyQualifiedName": "mlflow_prod"
}

displayName

Type: string Required: No Description: Human-readable display name

{
  "displayName": "MLflow Production Environment"
}

description (markdown)

Type: string (Markdown format) Required: No Description: Rich text description of the service's purpose and usage

{
  "description": "# MLflow Production\n\nProduction MLflow instance for tracking experiments, registering models, and serving predictions.\n\n## Usage\n- All production models must be registered here\n- Experiment tracking for data science team\n- Model deployment via SageMaker integration"
}

Service Configuration

serviceType (MLModelServiceType enum)

Type: string enum Required: Yes Allowed Values:

  • MLflow - Open-source ML lifecycle management
  • SageMaker - AWS SageMaker model registry
  • VertexAI - Google Cloud Vertex AI
  • AzureML - Azure Machine Learning
  • Databricks - Databricks ML
  • Kubeflow - Kubeflow on Kubernetes
  • CustomML - Custom ML platform
  • WandB - Weights & Biases
  • Neptune - Neptune.ai experiment tracking
  • H2O - H2O.ai AutoML
  • HuggingFace - Hugging Face model hub
{
  "serviceType": "MLflow"
}

connection (MLModelConnection)

Type: object Required: Yes Description: Connection configuration specific to the service type

MLflow Connection:

{
  "connection": {
    "config": {
      "type": "MLflow",
      "trackingUri": "https://mlflow.company.com",
      "registryUri": "https://mlflow.company.com",
      "supportsMetadataExtraction": true
    }
  }
}

AWS SageMaker Connection:

{
  "connection": {
    "config": {
      "type": "SageMaker",
      "awsRegion": "us-west-2",
      "awsAccessKeyId": "${AWS_ACCESS_KEY_ID}",
      "awsSecretAccessKey": "${AWS_SECRET_ACCESS_KEY}",
      "awsSessionToken": "${AWS_SESSION_TOKEN}",
      "endpointURL": "https://api.sagemaker.us-west-2.amazonaws.com"
    }
  }
}

Google Vertex AI Connection:

{
  "connection": {
    "config": {
      "type": "VertexAI",
      "gcpProjectId": "my-ml-project",
      "gcpRegion": "us-central1",
      "credentials": {
        "gcpConfig": {
          "type": "service_account",
          "projectId": "my-ml-project",
          "privateKeyId": "key-id",
          "privateKey": "-----BEGIN PRIVATE KEY-----\n...\n-----END PRIVATE KEY-----\n",
          "clientEmail": "ml-service@my-ml-project.iam.gserviceaccount.com"
        }
      }
    }
  }
}

Azure ML Connection:

{
  "connection": {
    "config": {
      "type": "AzureML",
      "subscriptionId": "azure-subscription-id",
      "resourceGroup": "ml-resource-group",
      "workspaceName": "ml-workspace",
      "tenantId": "azure-tenant-id",
      "clientId": "azure-client-id",
      "clientSecret": "${AZURE_CLIENT_SECRET}"
    }
  }
}

Governance Properties

owner (EntityReference)

Type: object Required: No Description: User or team that owns this service

{
  "owner": {
    "id": "9d8e7f6a-5b4c-3d2e-1f0a-9b8c7d6e5f4a",
    "type": "team",
    "name": "data-science",
    "displayName": "Data Science Team"
  }
}

domain (EntityReference)

Type: object Required: No Description: Data domain this service belongs to

{
  "domain": {
    "id": "a1b2c3d4-e5f6-7a8b-9c0d-1e2f3a4b5c6d",
    "type": "domain",
    "name": "AI-ML",
    "fullyQualifiedName": "AI-ML"
  }
}

tags[] (TagLabel[])

Type: array Required: No Description: Classification tags applied to the service

{
  "tags": [
    {
      "tagFQN": "Tier.Production",
      "description": "Production ML infrastructure",
      "source": "Classification",
      "labelType": "Manual",
      "state": "Confirmed"
    },
    {
      "tagFQN": "Environment.Prod",
      "source": "Classification",
      "labelType": "Manual",
      "state": "Confirmed"
    }
  ]
}

Versioning Properties

version (entityVersion)

Type: number Required: Yes (system-managed) Description: Metadata version number, incremented on changes

{
  "version": 1.2
}

updatedAt (timestamp)

Type: integer (Unix epoch milliseconds) Required: Yes (system-managed) Description: Last update timestamp

{
  "updatedAt": 1704240000000
}

updatedBy (string)

Type: string Required: Yes (system-managed) Description: User who made the update

{
  "updatedBy": "admin"
}

Complete Example

MLflow Production Service

{
  "id": "7a8b9c0d-1e2f-3a4b-5c6d-7e8f9a0b1c2d",
  "name": "mlflow_prod",
  "fullyQualifiedName": "mlflow_prod",
  "displayName": "MLflow Production",
  "description": "# MLflow Production\n\nProduction MLflow tracking server and model registry.\n\n## Purpose\n- Track experiments for all production models\n- Register validated models\n- Serve model versions via API",
  "serviceType": "MLflow",
  "connection": {
    "config": {
      "type": "MLflow",
      "trackingUri": "https://mlflow.company.com",
      "registryUri": "https://mlflow.company.com",
      "supportsMetadataExtraction": true
    }
  },
  "owner": {
    "id": "9d8e7f6a-5b4c-3d2e-1f0a-9b8c7d6e5f4a",
    "type": "team",
    "name": "data-science",
    "displayName": "Data Science Team"
  },
  "domain": {
    "id": "a1b2c3d4-e5f6-7a8b-9c0d-1e2f3a4b5c6d",
    "type": "domain",
    "name": "AI-ML"
  },
  "tags": [
    {"tagFQN": "Tier.Production"},
    {"tagFQN": "Environment.Prod"}
  ],
  "version": 1.0,
  "updatedAt": 1704240000000,
  "updatedBy": "admin"
}

SageMaker Service

{
  "id": "2b3c4d5e-6f7a-8b9c-0d1e-2f3a4b5c6d7e",
  "name": "sagemaker_models",
  "fullyQualifiedName": "sagemaker_models",
  "displayName": "AWS SageMaker Production",
  "description": "Production SageMaker instance for model deployment and serving",
  "serviceType": "SageMaker",
  "connection": {
    "config": {
      "type": "SageMaker",
      "awsRegion": "us-west-2",
      "awsAccessKeyId": "${AWS_ACCESS_KEY_ID}",
      "awsSecretAccessKey": "${AWS_SECRET_ACCESS_KEY}"
    }
  },
  "owner": {
    "id": "9d8e7f6a-5b4c-3d2e-1f0a-9b8c7d6e5f4a",
    "type": "team",
    "name": "ml-ops"
  },
  "tags": [
    {"tagFQN": "Tier.Production"},
    {"tagFQN": "Cloud.AWS"}
  ],
  "version": 1.0,
  "updatedAt": 1704240000000,
  "updatedBy": "admin"
}

RDF Representation

Ontology Class

@prefix om: <https://open-metadata.org/schema/> .
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
@prefix owl: <http://www.w3.org/2001/XMLSchema#> .

om:MLModelService a owl:Class ;
    rdfs:subClassOf om:Service ;
    rdfs:label "ML Model Service" ;
    rdfs:comment "An MLOps platform or model registry" ;
    om:hasProperties [
        om:name "string" ;
        om:serviceType "MLModelServiceType" ;
        om:connection "MLModelConnection" ;
        om:owner "Owner" ;
        om:tags "Tag[]" ;
    ] .

Instance Example

@prefix om: <https://open-metadata.org/schema/> .
@prefix ex: <https://example.com/services/> .

ex:mlflow_prod a om:MLModelService ;
    om:serviceName "mlflow_prod" ;
    om:fullyQualifiedName "mlflow_prod" ;
    om:displayName "MLflow Production" ;
    om:serviceType "MLflow" ;
    om:trackingUri "https://mlflow.company.com"^^xsd:anyURI ;
    om:ownedBy ex:data_science_team ;
    om:hasTag ex:tier_production ;
    om:hasTag ex:environment_prod ;
    om:hasMlModel ex:churn_predictor ;
    om:hasMlModel ex:fraud_detection .

JSON-LD Context

{
  "@context": {
    "@vocab": "https://open-metadata.org/schema/",
    "om": "https://open-metadata.org/schema/",
    "MLModelService": "om:MLModelService",
    "name": "om:serviceName",
    "fullyQualifiedName": "om:fullyQualifiedName",
    "displayName": "om:displayName",
    "serviceType": {
      "@id": "om:serviceType",
      "@type": "@vocab"
    },
    "connection": {
      "@id": "om:hasConnection",
      "@type": "@id"
    },
    "owner": {
      "@id": "om:ownedBy",
      "@type": "@id"
    },
    "tags": {
      "@id": "om:hasTag",
      "@type": "@id",
      "@container": "@set"
    }
  }
}

JSON-LD Example

{
  "@context": "https://open-metadata.org/context/mlmodelService.jsonld",
  "@type": "MLModelService",
  "@id": "https://example.com/services/mlflow_prod",
  "name": "mlflow_prod",
  "displayName": "MLflow Production",
  "serviceType": "MLflow",
  "owner": {
    "@id": "https://example.com/teams/data-science",
    "@type": "Team"
  },
  "tags": [
    {"@id": "https://open-metadata.org/tags/Tier/Production"}
  ]
}

Custom Properties

This entity supports custom properties through the extension field. Common custom properties include:

  • Data Classification: Sensitivity level
  • Cost Center: Billing allocation
  • Retention Period: Data retention requirements
  • Application Owner: Owning application/team

See Custom Properties for details on defining and using custom properties.

API Operations

Create ML Model Service

POST /api/v1/services/mlmodelServices
Content-Type: application/json

{
  "name": "mlflow_prod",
  "serviceType": "MLflow",
  "connection": {
    "config": {
      "type": "MLflow",
      "trackingUri": "https://mlflow.company.com"
    }
  }
}

Get ML Model Service

GET /api/v1/services/mlmodelServices/name/mlflow_prod?fields=owner,tags,domain

Update ML Model Service

PATCH /api/v1/services/mlmodelServices/{id}
Content-Type: application/json-patch+json

[
  {
    "op": "add",
    "path": "/tags/-",
    "value": {"tagFQN": "Tier.Production"}
  }
]

Test Connection

POST /api/v1/services/mlmodelServices/testConnection
Content-Type: application/json

{
  "serviceType": "MLflow",
  "connection": {
    "config": {
      "type": "MLflow",
      "trackingUri": "https://mlflow.company.com"
    }
  }
}

List Models in Service

GET /api/v1/mlmodels?service=mlflow_prod&fields=algorithm,owner