2025-11-03

Design an Enterprise AI Factory for GPUs, Data, and MLOps

AI is not “just another app” you park on general-purpose servers. Enterprise AI behaves like an ecosystem — volatile workloads, bursty data, exotic compute, and constant model evolution. That’s why so many AI pilots glow in the lab then die in production. The five tells that you’re no longer dealing with a normal workload: (1) you need horizontal scale, (2) accelerators like GPUs/TPUs matter, (3) data pipelines must flood continuously not trickle, (4) models mutate across versions and require versioning, observability and drift monitoring, (5) integration with legacy systems becomes the real bottleneck. The escape from “pilot / proof-of-concept death zone” is MLOps + orchestration — a Factory model — where DataOps, MLOps and GenAIOps operate from a unified command deck: templates, RBAC, private networking, GPU scheduling, AutoLake-style consistent data surfaces, and repeatability over ar

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Design an Enterprise AI Factory for GPUs, Data, and MLOps (English)