{
  "id": "lib-process:data-science-ml--model-monitoring-drift",
  "_kind": "LibraryProcess",
  "_file": "generated-library/processes.yaml",
  "_cluster": "generated-library",
  "attributes": {
    "displayName": "model-monitoring-drift",
    "description": "Model Performance Monitoring and Drift Detection - Continuously monitor prediction accuracy,\ndetect data drift and concept drift, track feature distributions, alert on performance degradation, and\ntrigger automated retraining workflows with comprehensive quality gates.",
    "libraryPath": "library/specializations/data-science-ml/model-monitoring-drift.js",
    "specialization": "data-science-ml",
    "references": [
      "- Evidently AI Model Monitoring: https://www.evidentlyai.com/\n- Arize AI ML Observability: https://arize.com/\n- WhyLabs AI Observability: https://whylabs.ai/\n- MLOps Continuous Monitoring: https://cloud.google.com/architecture/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning\n- Concept Drift Detection: https://arxiv.org/abs/1010.4784\n- Data Drift in ML: https://towardsdatascience.com/understanding-dataset-shift-f2a5a262a766"
    ],
    "example": "const result = await orchestrate('specializations/data-science-ml/model-monitoring-drift', {\n  modelId: 'fraud-detection-v3',\n  monitoringWindowDays: 7,\n  driftThresholds: {\n    dataDrift: 0.15,\n    conceptDrift: 0.10,\n    featureDrift: 0.20\n  },\n  performanceThresholds: {\n    accuracyDrop: 0.05,\n    precisionDrop: 0.03,\n    latencyIncrease: 1.5,\n    errorRateIncrease: 0.02\n  },\n  alertChannels: ['slack', 'pagerduty', 'email']\n});",
    "usesAgents": [
      "general-purpose"
    ]
  },
  "outgoingEdges": [
    {
      "from": "lib-process:data-science-ml--model-monitoring-drift",
      "to": "domain:data-science",
      "kind": "lib_applies_to_domain",
      "attributes": {
        "weight": 1
      }
    },
    {
      "from": "lib-process:data-science-ml--model-monitoring-drift",
      "to": "role:data-scientist",
      "kind": "lib_involves_role",
      "attributes": {
        "weight": 1
      }
    },
    {
      "from": "lib-process:data-science-ml--model-monitoring-drift",
      "to": "workflow:data-pipeline-deployment",
      "kind": "lib_implements_workflow",
      "attributes": {
        "weight": 1
      }
    },
    {
      "from": "lib-process:data-science-ml--model-monitoring-drift",
      "to": "specialization:data-science-ml",
      "kind": "lib_belongs_to_specialization",
      "attributes": {
        "weight": 0.9
      }
    }
  ],
  "incomingEdges": []
}