How do you organize ML teams (embedded vs central)?

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Answer

$Central ML team: shared expertise, owns platform + tooling; downside: bottleneck + disconnected from product.$
$Embedded: ML engineers in each product team; direct problem focus; downside: duplication + platform fragmentation.$
$Hybrid (best practice): central platform team (feature store, serving infra, monitoring) + embedded ML engineers per product using platform.$
$Similar to DevOps \to SRE evolution.$
$Ownership: 'you build it, you run it' — model owners on-call.$

Check yourself — multiple choice

Random
Central: shared expertise + platform, but bottleneck; Embedded: product-focused but duplication; Hybrid best: central platform + embedded ML per product; SRE-like evolution; model owners on-call
Only central
Not real

Team org: hybrid (central platform + embedded ML per product) wins.

#mlops

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