The Head of AI/ML's Cloud Stack

What a Head of AI/ML actually owns — the ML platform foundation, the MLOps discipline, the responsible-AI guardrails, and the generative-AI operating model that takes RAG and agents from notebook to production.

BusinessCapabilityTechnology
Compass
  • Businesspersona, use case, outcome
  • Capabilitywhat the org needs to do
  • Technologythe technology choices
Guided journey · Step 1 of 4

ML Platform Foundation

Stand up the ML platform foundation — registry, pipelines, deployment, monitoring. Notebook-to-production needs more than notebooks.

~ 12 weeks

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Narrative intro

The Head of AI/ML is the operational owner of AI getting to production. This map names the four operational pillars — ML platform, MLOps, responsible AI, GenAI operations — and the SKU choices that anchor each. The Foundry vs Mosaic AI decision is workload-fit, not vendor preference; the Copilot Studio boundary is low-code vs pro-code.

Key takeaways

  • ML platform foundation precedes MLOps discipline — you can't operate what isn't deployed
  • MLOps is a cadence story — versioning, drift, retraining as rhythms
  • Responsible AI belongs in the build pipeline as gates, not as audits
  • Foundry vs Mosaic AI is workload-fit; Foundry vs Copilot Studio is pro-code vs low-code

Programme shape

Estimated duration
20–40 weeks
Estimated FTE
1 FTE ML platform lead + part-time data, security, and responsible-AI SMEs
Spend tier
significant
Risk level
elevated

Responsible-AI tooling needs to be a build-time discipline. Cost surprises live in the LLM token line.

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