{
  "id": "lib-process:data-science-ml--ml-integration-testing",
  "_kind": "LibraryProcess",
  "_file": "generated-library/processes.yaml",
  "_cluster": "generated-library",
  "attributes": {
    "displayName": "ml-integration-testing",
    "description": "ML System Integration Testing - Validate end-to-end ML pipeline integration across data ingestion,\npreprocessing, model training, serving, and monitoring components with quality gates and validation loops.",
    "libraryPath": "library/specializations/data-science-ml/ml-integration-testing.js",
    "specialization": "data-science-ml",
    "references": [
      "- ML Testing: A Guide: https://madewithml.com/courses/mlops/testing/\n- Google ML Testing Best Practices: https://developers.google.com/machine-learning/testing-debugging\n- AWS ML Testing: https://aws.amazon.com/blogs/machine-learning/testing-approaches-for-amazon-sagemaker-ml-models/\n- Microsoft ML Testing: https://learn.microsoft.com/en-us/azure/architecture/guide/testing/mission-critical-deployment-testing\n- Integration Testing Patterns: https://martinfowler.com/articles/microservice-testing/\n- ML Observability: https://neptune.ai/blog/ml-model-testing"
    ],
    "example": "const result = await orchestrate('specializations/data-science-ml/ml-integration-testing', {\n  systemName: 'Recommendation Engine',\n  components: ['data-pipeline', 'feature-store', 'model-service', 'monitoring'],\n  testEnvironment: 'staging',\n  integrationScenarios: [\n    { name: 'end-to-end-prediction', type: 'e2e' },\n    { name: 'model-update-rollout', type: 'deployment' },\n    { name: 'data-drift-detection', type: 'monitoring' }\n  ],\n  performanceRequirements: {\n    latency: { p95: 100, p99: 200 },\n    throughput: { min: 1000 },\n    accuracy: { min: 0.85 }\n  },\n  targetCoverage: 85\n});",
    "usesAgents": [
      "general-purpose"
    ]
  },
  "outgoingEdges": [
    {
      "from": "lib-process:data-science-ml--ml-integration-testing",
      "to": "domain:data-science",
      "kind": "lib_applies_to_domain",
      "attributes": {
        "weight": 1
      }
    },
    {
      "from": "lib-process:data-science-ml--ml-integration-testing",
      "to": "workflow:code-review",
      "kind": "lib_implements_workflow",
      "attributes": {
        "weight": 1
      }
    },
    {
      "from": "lib-process:data-science-ml--ml-integration-testing",
      "to": "workflow:ml-model-lifecycle",
      "kind": "lib_implements_workflow",
      "attributes": {
        "weight": 0.7
      }
    },
    {
      "from": "lib-process:data-science-ml--ml-integration-testing",
      "to": "specialization:data-science-ml",
      "kind": "lib_belongs_to_specialization",
      "attributes": {
        "weight": 0.9
      }
    }
  ],
  "incomingEdges": []
}