MLOps
Quick Answer
MLOps is the discipline of operationalising machine learning: the practices, tools and culture needed to deploy, monitor, retrain and govern models reliably in production. It extends DevOps thinking to the unique challenges of data and models.
In Depth
What MLOps really means
Core MLOps capabilities include experiment tracking, versioned data and models, automated retraining pipelines, deployment infrastructure, observability for drift and performance, and governance workflows for model approval and decommissioning.
MLOps maturity is often the deciding factor between AI pilots that die on the vine and those that scale across an organisation. Investing in MLOps earlier — even at a basic level — pays dividends as the portfolio of models grows.
Why It Matters
Business relevance for UK organisations
UK organisations with more than a handful of models in production almost always underinvest in MLOps. The symptom is usually models that silently decay, triggering incidents that erode trust in AI.
Real-world example
How this shows up in practice
A Leeds insurer introduced lightweight MLOps tooling and caught a pricing model drifting by 6% within 11 days — an issue its previous process would not have flagged for months.
Related Terms
Continue exploring
Model
A model is the trained output of a machine learning process — a collection of learned parameters that, combined with an algorithm, can turn new inputs into predictions or generated content without being explicitly programmed for each case.
BasicsInference
Inference is the phase in which a trained model is used to produce predictions or outputs on new data. While training happens once (or periodically), inference happens every time a user interacts with an AI system, making it the dominant cost in most production deployments.
BasicsTraining Data
Training data is the dataset used to teach a machine learning model the patterns it needs to perform its task. The quality, quantity, diversity and recency of training data directly determine how accurate and fair the resulting model will be.
BusinessAI Governance
AI governance is the set of policies, roles, controls and oversight mechanisms that ensure AI is used responsibly, safely and in line with law and organisational values. Effective governance is proportionate — tight where risk is high, light where risk is low.