iiRecord
Agentic AI Atlas · Python ML Stack (NumPy, Pandas, scikit-learn)
stack-profile:python-ml-stacka5c.ai
II.
StackProfile JSON

stack-profile:python-ml-stack

Structured · live

Python ML Stack (NumPy, Pandas, scikit-learn) json

Inspect the normalized record payload exactly as the atlas UI reads it.

File · domain/stack-profiles/stack-profiles-additional.yamlCluster · domain
Record JSON
{
  "id": "stack-profile:python-ml-stack",
  "_kind": "StackProfile",
  "_file": "domain/stack-profiles/stack-profiles-additional.yaml",
  "_cluster": "domain",
  "attributes": {
    "displayName": "Python ML Stack (NumPy, Pandas, scikit-learn)",
    "description": "The classical Python machine learning stack anchored by NumPy for\nnumerical array operations, Pandas for tabular data manipulation and\nexploration, and scikit-learn for preprocessing, model selection, and\na broad catalogue of supervised and unsupervised algorithms.\n\nThis combination is the default starting point for data science and\napplied ML work. Matplotlib and Seaborn are typically added for\nvisualisation. Jupyter notebooks serve as the interactive development\nenvironment. When models need to graduate to production APIs, FastAPI\nor Flask are layered on top. scikit-learn's consistent estimator API\nand Pipeline abstraction make feature engineering and model evaluation\nstraightforward to compose and cross-validate.\n",
    "composes": [
      "language:python",
      "library:numpy",
      "library:pandas",
      "library:scikit-learn"
    ]
  },
  "outgoingEdges": [
    {
      "from": "stack-profile:python-ml-stack",
      "to": "language:python",
      "kind": "composed_of"
    },
    {
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      "to": "library:numpy",
      "kind": "composed_of"
    },
    {
      "from": "stack-profile:python-ml-stack",
      "to": "library:pandas",
      "kind": "composed_of"
    },
    {
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      "to": "library:scikit-learn",
      "kind": "composed_of"
    },
    {
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      "to": "library:matplotlib",
      "kind": "composed_of"
    },
    {
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      "to": "library:seaborn",
      "kind": "composed_of"
    },
    {
      "from": "stack-profile:python-ml-stack",
      "to": "role:data-scientist",
      "kind": "used_by_role"
    },
    {
      "from": "stack-profile:python-ml-stack",
      "to": "role:ml-engineer",
      "kind": "used_by_role"
    },
    {
      "from": "stack-profile:python-ml-stack",
      "to": "role:research-scientist",
      "kind": "used_by_role"
    },
    {
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      "to": "domain:data-science",
      "kind": "applies_to"
    },
    {
      "from": "stack-profile:python-ml-stack",
      "to": "domain:machine-learning",
      "kind": "applies_to"
    },
    {
      "from": "stack-profile:python-ml-stack",
      "to": "skill-area:machine-learning-frameworks",
      "kind": "requires_skill_area"
    },
    {
      "from": "stack-profile:python-ml-stack",
      "to": "skill-area:data-preprocessing",
      "kind": "requires_skill_area"
    },
    {
      "from": "stack-profile:python-ml-stack",
      "to": "skill-area:statistical-analysis",
      "kind": "requires_skill_area"
    },
    {
      "from": "stack-profile:python-ml-stack",
      "to": "skill-area:data-visualization",
      "kind": "requires_skill_area"
    },
    {
      "from": "stack-profile:python-ml-stack",
      "to": "skill-area:feature-engineering",
      "kind": "requires_skill_area"
    }
  ],
  "incomingEdges": []
}