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01/08/2026 10:43 pm
TL;DR: The ML infrastructure stack breaks the pipeline into data, model and deployment layers, showing how tool choice, team handoff and monitoring shape model performance and operational risk, according to Openlayer. For practitioners, the governance lesson is that ML systems become identity and access systems as soon as model artifacts, feature stores and deployment tooling need controlled access.
NHIMG editorial — based on content published by Openlayer: Understanding each piece in the ML infrastructure stack
Questions worth separating out
Q: How should security teams govern access to ML infrastructure stacks?
A: Security teams should treat the ML stack as a set of connected identity domains, not as a collection of tools.
Q: Why do ML pipelines create hidden identity risk?
A: ML pipelines create hidden identity risk because automation, reuse, and cross-team handoffs encourage long-lived credentials and inherited permissions.
Q: How do you know if ML access governance is actually working?
A: It is working when the organisation can answer three questions quickly: who owns each pipeline identity, which credentials are still active, and which artefacts are approved for production use.
Practitioner guidance
- Inventory ML pipeline identities Map human users, service accounts, API keys, and workload identities across data, model, and deployment layers.
- Apply lifecycle rules to model artefacts Tie model registry entries, feature-store records, and deployment credentials to explicit owners, expiry dates, and offboarding triggers.
- Separate experimentation from production access Use distinct credentials and approval paths for notebooks, training jobs, and live model-serving services.
What's in the full article
Openlayer's full article covers the operational detail this post intentionally leaves for the source:
- Specific tool examples across data processing, experimentation, registries, serving, and monitoring for ML teams building the stack.
- Practical breakdowns of how feature stores, model registries, and deployment platforms fit together in day-to-day delivery.
- Implementation-oriented explanations of model serving and monitoring choices for teams moving from experimentation to production.
- Source article context on the role of MLOps in reducing handoff friction between data science and operations teams.
👉 Read Openlayer's guide to the ML infrastructure stack and its components →
ML infrastructure stack governance: what IAM teams should notice?
Explore further
01/08/2026 10:53 pm
ML infrastructure governance is becoming an identity governance problem. The article treats the stack as a set of functional layers, but the real operational risk is the identity fabric tying them together. Data stores, notebooks, registries, and deployment platforms all depend on accounts, tokens, and service credentials. When those identities are not lifecycle-managed, the ML stack inherits the same standing-access problems that identity teams already see in other automation environments. Practitioners should therefore govern the stack as a connected entitlement system, not as separate engineering tools.
A question worth separating out:
Q: What should teams do when ML artefacts are retired?
A: Teams should revoke the access that exists only because the artefact once mattered. That includes serving credentials, registry permissions, and any shared secrets tied to the model or pipeline. Retirement should trigger offboarding just as deliberately as user departure, because unused artefacts often keep usable trust paths.
👉 Read our full editorial: ML infrastructure stack governance is now an identity problem
