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ML training pipeline

Raw data → cleaning → features → split → train → evaluate → deploy.

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{
  "nodes": [
    { "id":"raw","type":"rect","label":"Raw data","x":40,"y":160,"fill":"#e2e8f0","stroke":"#475569" },
    { "id":"cln","type":"rect","label":"Cleaning","x":220,"y":160,"fill":"#dbeafe","stroke":"#2563eb" },
    { "id":"fe","type":"rect","label":"Feature engineering","x":420,"y":160,"fill":"#ede9fe","stroke":"#6d28d9" },
    { "id":"sp","type":"diamond","label":"Split","x":640,"y":145,"fill":"#fef3c7","stroke":"#a16207" },
    { "id":"tr","type":"rect","label":"Train","x":820,"y":80,"fill":"#fed7aa","stroke":"#c2410c" },
    { "id":"vl","type":"rect","label":"Validate","x":820,"y":240,"fill":"#fce7f3","stroke":"#be185d" },
    { "id":"ev","type":"diamond","label":"Pass?","x":1020,"y":145,"fill":"#fef3c7","stroke":"#a16207" },
    { "id":"dep","type":"ellipse","label":"Deploy","x":1220,"y":80,"fill":"#dcfce7","stroke":"#15803d" },
    { "id":"itr","type":"rect","label":"Iterate","x":1220,"y":240,"fill":"#fee2e2","stroke":"#b91c1c" }
  ],
  "edges": [
    { "id":"e1","source":"raw","target":"cln" },
    { "id":"e2","source":"cln","target":"fe" },
    { "id":"e3","source":"fe","target":"sp" },
    { "id":"e4","source":"sp","target":"tr","label":"train" },
    { "id":"e5","source":"sp","target":"vl","label":"val/test" },
    { "id":"e6","source":"tr","target":"ev" },
    { "id":"e7","source":"vl","target":"ev" },
    { "id":"e8","source":"ev","target":"dep","label":"yes" },
    { "id":"e9","source":"ev","target":"itr","label":"no" },
    { "id":"e10","source":"itr","target":"fe" }
  ]
}