MLOps & AIOps
Two disciplines that are reshaping how organisations build and operate intelligent systems at scale — and how IT keeps up with the speed of AI.
What is MLOps?
MLOps (Machine Learning Operations) is the set of practices, tools, and cultural norms that enable organisations to reliably and efficiently build, deploy, monitor, and improve machine learning models in production.
It applies the principles of DevOps — automation, continuous delivery, observability, and collaboration — to the ML model lifecycle.
| Without MLOps | With MLOps |
|---|---|
| Models built in notebooks, never deployed | Models move from experiment to production reliably |
| No versioning — "which model is in prod?" | Full lineage: data → experiment → model → deployment |
| Manual retraining triggered by complaints | Automated drift detection and retraining pipelines |
| Data scientists and ops teams work in silos | Shared ownership across data science, ML engineering, and platform teams |
| Governance is an afterthought | Model cards, bias checks, and audit trails built into the pipeline |
What is AIOps?
AIOps (Artificial Intelligence for IT Operations) applies AI and ML to IT operations data — logs, metrics, events, traces — to detect anomalies, correlate incidents, identify root causes, and automate remediation.
Where MLOps is about building and running AI systems, AIOps is about using AI to run IT systems better.
| Capability | What it does |
|---|---|
| Anomaly detection | Identifies unusual patterns in metrics/logs before they become incidents |
| Event correlation | Groups related alerts into a single actionable incident, reducing noise |
| Root cause analysis | Traces the origin of an incident across a complex distributed system |
| Predictive alerting | Forecasts capacity exhaustion or service degradation before it occurs |
| Automated remediation | Triggers runbooks or scripts autonomously for known error patterns |
| Change impact analysis | Predicts which services will be affected by a planned change |
MLOps vs AIOps — side by side
| Dimension | MLOps | AIOps |
|---|---|---|
| Primary goal | Reliable ML model delivery and operations | AI-augmented IT operations |
| Who uses it | Data scientists, ML engineers, platform teams | SREs, NOC teams, ITSM practitioners |
| Data inputs | Training data, feature stores, model metrics | Logs, metrics, events, traces, CMDB |
| Key outputs | Deployed models, model performance reports | Fewer incidents, faster MTTR, automated remediations |
| ITIL 5 alignment | Build, Transition, Operate activities + AI Capability Model (C1–C6) | Operate, Support activities + C2 Curation, C4 Cognition, C6 Coordination |
| Tooling | MLflow, Kubeflow, SageMaker, Vertex AI | Dynatrace, BigPanda, ServiceNow AIOps, Datadog |
Connection to ITIL 5
ITIL 5 introduces the AI Capability Model (6C) — a classification of six AI capabilities that product and service teams can apply across the Product & Service Lifecycle Model (PSLM).
MLOps and AIOps map directly to this model:
| ITIL 5 AI Capability | MLOps application | AIOps application |
|---|---|---|
| C1 — Creation | AI-generated code scaffolding, test generation, model documentation | Auto-generated runbooks, post-incident reports |
| C2 — Curation | Feature selection, data quality filtering, experiment ranking | Alert filtering, noise reduction, signal prioritisation |
| C3 — Clarification | Natural language requirements → model specs | NLP-based ticket classification, intent extraction |
| C4 — Cognition | Model risk scoring, drift impact assessment | Predictive change risk, root cause reasoning |
| C5 — Communication | AI-generated model performance summaries | Conversational AI for first-line support, status updates |
| C6 — Coordination | Orchestrated multi-step ML pipelines (train → validate → deploy) | Autonomous remediation workflows with human approval gates |
The ITIL 5 Product & Service Lifecycle Model treats both MLOps and AIOps as disciplines that operate within and across the PSLM phases — not as replacements for ITSM practices.
Why both matter for MENA and European organisations
Government & public sector: AI regulations (EU AI Act, UAE AI Strategy, Saudi National AI Strategy) require explainability, auditability, and governance throughout the model lifecycle — exactly what MLOps provides. AIOps supports the high-availability requirements of digital government services.
Telecoms & critical infrastructure: AIOps enables predictive maintenance and event correlation at scale. MLOps allows operators to continuously retrain fraud detection and network optimisation models without manual intervention.
Banking & financial services: Model risk management regulations (CBUAE, SAMA, EBA) require documented model validation, performance monitoring, and rollback capability — core MLOps disciplines. AIOps accelerates incident response for trading and payment systems.
Getting started
If you're new to these disciplines, the recommended entry path is:
- Assess your current state — do you have reproducible model training? Do you know which model is in production? Can you detect when a model degrades?
- Start with observability — instrument your ML pipelines and your IT estate before automating anything.
- Apply ITIL 5 governance — map MLOps and AIOps activities to the PSLM and ensure AI governance (accountability, explainability, bias controls) is built in from the start.
- Automate incrementally — standard changes first, then normal changes. Don't automate before you've optimised (ITIL 5 Guiding Principle: Optimize and automate).
Next: MLOps Practices → · AIOps Practices → · Tools & Platforms →