What MLOps means in practice
MLOps is to ML what DevOps is to software: the discipline of moving models from notebook to production reliably and repeatably. Versioning of data, models and code; reproducible training; automated deployment; monitoring of drift, latency and accuracy; rollback when something regresses; lineage from inference back to training data. For generative AI the discipline picks up additional concerns — prompt versioning, evaluation against an SME-built test set on every change, A/B testing of prompt or model variants. The point is not the tooling (Weights & Biases, MLflow, Langfuse, Kubeflow); it is the operational maturity that lets a team upgrade a model on a Tuesday afternoon without a Friday-night incident.
Related terms
Evaluation (evals) →
Systematic testing of an AI system against a curated set of inputs to measure quality on the dimensions the business cares about — accuracy, faithfulness, safety, format.
Drift (data + model) →
The phenomenon where a model's input distribution or its accuracy degrades over time as the world it predicts about changes.
LLM observability →
Capturing every LLM call (prompt, retrieved context, response, latency, cost, user feedback) in a structured store so production behaviour can be inspected and improved.
The Sovereign AI Playbook.
Enterprise generative AI that runs on your servers.
- 01Definition. The four non-negotiable properties of a sovereign deployment — and the three patterns that look sovereign but aren't.
- 02Why now. Three forces that converged in 2026: regulator pressure, the open-weights capability gap closing, and unit economics flipping.
- 03Reference architecture. The five-layer stack we deploy: applications → agent runtime → sovereign LLM → RAG + KB → data stores. With model and tooling choices for each.
- 04The regulators. SAMA · RBI · UK ICO · EU AI Act · DPDP · HIPAA — what each requires and how it maps to architecture.
All 62 terms, in plain language
Sovereign AI, RAG, agentic AI, IDP, MLOps and the regulations that shape enterprise AI.