{
  "id": "stack-profile:feature-store-mlops",
  "_kind": "StackProfile",
  "_file": "domain/stack-profiles/deep-stacks-1.yaml",
  "_cluster": "domain",
  "attributes": {
    "displayName": "Feature Store & MLOps Stack (Feast, MLflow, BentoML, K8s, Prometheus)",
    "description": "A production MLOps platform centered on feature management and model\nlifecycle governance. Feast provides a feature store that bridges\noffline training (batch features from the warehouse) and online serving\n(low-latency feature lookups via Redis or DynamoDB), ensuring\ntrain-serve consistency.\n\nMLflow tracks experiments, registers model versions, and manages the\npromotion pipeline from staging to production. BentoML packages trained\nmodels into containerized inference services with adaptive batching\nand multi-model serving. Kubernetes orchestrates both training jobs and\nserving deployments with autoscaling. Prometheus monitors inference\nlatency, feature freshness, and data drift. Choose this stack when\nyour ML team needs reproducible pipelines, governed model promotion,\nand feature reuse across multiple models.\n",
    "composes": [
      "tool:feast",
      "tool:mlflow",
      "tool:bentoml",
      "tool:kubernetes",
      "tool:prometheus",
      "language:python"
    ]
  },
  "outgoingEdges": [
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      "kind": "composed_of"
    },
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      "kind": "composed_of"
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    },
    {
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      "to": "library:redis",
      "kind": "composed_of"
    },
    {
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      "to": "role:ml-ops-engineer",
      "kind": "used_by_role"
    },
    {
      "from": "stack-profile:feature-store-mlops",
      "to": "role:ml-engineer",
      "kind": "used_by_role"
    },
    {
      "from": "stack-profile:feature-store-mlops",
      "to": "role:data-engineer",
      "kind": "used_by_role"
    },
    {
      "from": "stack-profile:feature-store-mlops",
      "to": "workflow:model-deployment-pipeline",
      "kind": "follows_workflow"
    },
    {
      "from": "stack-profile:feature-store-mlops",
      "to": "workflow:feature-store-management",
      "kind": "follows_workflow"
    },
    {
      "from": "stack-profile:feature-store-mlops",
      "to": "domain:ml-ops",
      "kind": "applies_to"
    },
    {
      "from": "stack-profile:feature-store-mlops",
      "to": "domain:machine-learning",
      "kind": "applies_to"
    },
    {
      "from": "stack-profile:feature-store-mlops",
      "to": "skill-area:feature-engineering-pipelines",
      "kind": "requires_skill_area"
    },
    {
      "from": "stack-profile:feature-store-mlops",
      "to": "skill-area:model-registry-management",
      "kind": "requires_skill_area"
    },
    {
      "from": "stack-profile:feature-store-mlops",
      "to": "skill-area:model-serving-deployment",
      "kind": "requires_skill_area"
    },
    {
      "from": "stack-profile:feature-store-mlops",
      "to": "skill-area:ci-cd-ml-pipelines",
      "kind": "requires_skill_area"
    },
    {
      "from": "stack-profile:feature-store-mlops",
      "to": "skill-area:model-monitoring-drift-detection",
      "kind": "requires_skill_area"
    }
  ],
  "incomingEdges": []
}