AI/ML Operations for Data Leaders

Narrative intro

AI and machine learning have moved from experimentation to production reality at most large enterprises. Models in notebooks aren't the question any more — reliable production services, drift detection, model risk management, and the distinct operational discipline that generative AI requires are. The Head of AI/ML role exists because traditional MLOps doesn't cover LLMs, RAG, evaluation, and prompt injection — and traditional data science doesn't cover model risk frameworks or EU AI Act compliance. Microsoft Azure AI Foundry, Databricks Mosaic AI, and Snowflake Cortex all provide credible production AI platforms. The choice is integration with the broader data estate. The operational discipline matters more than the platform choice: drift detection that actually catches drift, evaluation pipelines that produce measurable quality signals, model governance that survives EU AI Act enforcement, prompt injection defence that prevents the canonical adversarial pattern. This map covers the four pillars that make AI/ML at production scale a defensible programme rather than a fragile pile of experiments.

Key takeaways

• Generative AI operations is a distinct discipline from traditional MLOps. LLM lifecycle, RAG, evaluation, prompt injection defence are first-class operational concerns.
• ML platform foundation — model registry, feature store, experiment tracking — is the prerequisite that every later pillar depends on. Skip it and every team builds its own.
• EU AI Act enforcement is staged through 2026–2027 and produces real penalties. Responsible AI as documentation produces audit failures; as discipline, it produces a defensible programme.
• LLM token cost scales rapidly with adoption. Monitor per workload, budget per use case, build the cost-discipline before the bill arrives.

Programme shape

Estimated duration

24–60 weeks

Estimated FTE

Head of AI/ML, ML platform engineers, data scientists, ML engineers, MLOps engineers, responsible AI lead, regulatory liaison. AI/ML at enterprise scale is a multi-function operation; the FTE shape reflects the standing team.

Spend tier

significant

Risk level

elevated

Risk shifts to high if responsible AI is treated as a documentation exercise rather than a working discipline — EU AI Act enforcement is staged through 2026–2027 and produces real penalties. Cost shape can surprise rapidly with generative AI workloads; budget per workload, not per organisation. Most ML programme failures are operational rather than technical — manual deployment, no drift detection, no model governance.

Source references

• Azure AI Foundry documentation — Canonical Azure AI Foundry docs — model catalog, agents, evaluation, deployment.
• Databricks Mosaic AI documentation — Databricks ML and Mosaic AI — model lifecycle, generative AI on Databricks.
• EU AI Act regulatory text — EU AI Act enforcement timeline and obligations. Staged through 2026–2027.
• NIST AI Risk Management Framework — NIST AI RMF — the US voluntary standard that increasingly anchors enterprise AI governance.