MLOps

What it is

MLOps (Machine Learning Operations) is the set of practices, tools, and organisational disciplines used to take machine learning models from experimentation to reliable, repeatable production. It applies the automation and rigour of DevOps to the specific challenges of ML: data pipelines, model training, versioning, deployment, and continuous monitoring.

Unlike traditional software, an ML system depends on data as well as code. A model that worked last quarter can silently degrade as customer behaviour, prices, or market conditions shift. MLOps exists to manage this moving target.

Why it matters

Most ML projects never reach production, and many that do fail quietly. MLOps addresses the gap between a promising prototype and a dependable business capability.

• Reliability: models behave predictably and can be rolled back.
• Reproducibility: any result can be traced to specific data, code, and parameters.
• Speed: new versions ship in hours or days, not months.
• Governance: audit trails, access control, and documented lineage support compliance.
• Cost control: automated retraining and monitoring prevent expensive silent failures.

How it is used in practice

A mature MLOps setup typically covers:

• Data and feature pipelines: versioned, validated, and repeatable.
• Experiment tracking: recording parameters, metrics, and artefacts.
• Model registry: a catalogue of approved model versions and their status.
• CI/CD for models: automated testing, packaging, and deployment.
• Monitoring: tracking latency, accuracy, and data drift (inputs changing over time).
• Automated retraining: triggered by schedule or by detected drift.

These practices apply equally to classic predictive models and to applied LLM systems (where prompts, retrieval sources, and evaluation sets also need versioning and monitoring, sometimes called LLMOps).

A concrete worked example

A bank deploys a credit scoring model.

1. Data scientists train a model; the run is logged with its dataset version and metrics.

2. The model passes automated fairness and accuracy tests, then enters the registry as "staging".

3. After human approval it is promoted to "production" and served via an API.

4. Monitoring flags that applicant income distributions have shifted (drift), and approval rates are climbing.

5. An automated pipeline retrains on fresh data, the new version is tested, and it replaces the old one with full audit history.

Without MLOps, step 4 might go unnoticed until losses or a regulator surface the problem.