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Reference architecture for an AI/ML Internal Developer Platform on GCP

As enterprises race to operationalize AI beyond scattered pilots, this GCP reference architecture distills hard-won lessons from real platform teams into a practical blueprint for an AI/ML Internal Developer Platform. It rethinks the stack as six modular “planes” to keep fast-moving ML tooling governable, reproducible, and scalable, secure-by-default, automation-first, and built to deliver compliant models to production with the same consistency modern IDPs bring to software.

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This whitepaper explains how enterprises can move from isolated AI/ML pilots to secure, repeatable, cost-controlled production by building a purpose-built Internal Developer Platform (IDP) for Data/AI/ML on Google Cloud. You’ll learn:

Why AI/ML efforts stall at the pilot stage: fragmented tooling, data complexity, reproducibility gaps, monitoring blind spots, security/compliance risk, and runaway GPU/compute costs are the recurring blockers.
What makes an AI/ML IDP different from a traditional software IDP: AI/ML adds notebooks, multi-persona workflows (data scientists, ML engineers, data engineers, platform teams), complex data/model dependencies, and stricter governance needs; the platform’s main job is reducing cognitive load and enabling MLOps at scale.
The proposed “planes not layers” architecture: six cross-cutting planes replace rigid stack layers to keep the platform modular, evolvable, and resilient to fast-changing ML tooling.
How “golden paths” make adoption real: opinionated, templated workflows that teams can follow for fast, governed delivery.
Organizational and rollout guidance: success requires product-mindset ownership, cross-functional alignment, and starting with high-impact pilots that prove value before scaling.

Why AI/ML efforts stall at the pilot stage: fragmented tooling, data complexity, reproducibility gaps, monitoring blind spots, security/compliance risk, and runaway GPU/compute costs are the recurring blockers.
What makes an AI/ML IDP different from a traditional software IDP: AI/ML adds notebooks, multi-persona workflows (data scientists, ML engineers, data engineers, platform teams), complex data/model dependencies, and stricter governance needs; the platform’s main job is reducing cognitive load and enabling MLOps at scale.
The proposed “planes not layers” architecture: six cross-cutting planes replace rigid stack layers to keep the platform modular, evolvable, and resilient to fast-changing ML tooling.
How “golden paths” make adoption real: opinionated, templated workflows that teams can follow for fast, governed delivery.
Organizational and rollout guidance: success requires product-mindset ownership, cross-functional alignment, and starting with high-impact pilots that prove value before scaling.

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