{
  "id": "stack-profile:ml-pipeline-stack",
  "_kind": "StackProfile",
  "_file": "domain/stack-profiles/common-stacks.yaml",
  "_cluster": "domain",
  "attributes": {
    "displayName": "ML Pipeline Stack (PyTorch/TensorFlow, MLflow, BentoML, K8s)",
    "description": "A production machine learning pipeline stack: PyTorch or TensorFlow\nas the training framework, MLflow for experiment tracking, model\nregistry, and reproducibility, BentoML for packaging models into\ndeployable services, Kubernetes for orchestrating training jobs and\nserving endpoints, and Prometheus for monitoring model performance\nand infrastructure health.\n\nThe pipeline spans from data preparation through training,\nevaluation, packaging, deployment, and monitoring. MLflow tracks\nhyperparameters, metrics, and artifacts across experiments. BentoML\nwraps trained models into containerized REST/gRPC services with\nadaptive batching. Kubernetes provides autoscaling for both training\n(via jobs or operators like KubeFlow) and inference (via\nDeployments with HPA). Prometheus scrapes model latency, throughput,\nand data-drift metrics. This stack is common in organizations that\nhave graduated beyond notebook-driven ML and need repeatable,\nobservable, production-grade model lifecycle management.\n",
    "composes": [
      "language:python",
      "library:pytorch",
      "library:tensorflow",
      "tool:mlflow",
      "tool:bentoml",
      "tool:kubernetes",
      "tool:prometheus",
      "tool:docker"
    ]
  },
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      "kind": "used_by_role"
    },
    {
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      "kind": "used_by_role"
    },
    {
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      "to": "role:data-scientist",
      "kind": "used_by_role"
    },
    {
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      "kind": "applies_to"
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      "kind": "requires_skill_area"
    },
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      "kind": "requires_skill_area"
    },
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      "kind": "requires_skill_area"
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      "kind": "requires_skill_area"
    }
  ],
  "incomingEdges": []
}