Infrastructure & Deployment

MLOps

The practice of managing AI models in production — deployment, monitoring, updating.

Definition

MLOps (Machine Learning Operations) applies software engineering and DevOps principles to the lifecycle of AI models: deploying them reliably, monitoring their performance, detecting and responding to drift, updating them safely, and rolling back when problems occur. As organisations move from AI experiments to production AI, MLOps becomes essential for maintaining quality and reliability at scale. It covers tooling, processes, and organisational practices.

Why this matters for your business

Many organisations underestimate the operational complexity of running AI in production. MLOps investment ensures AI systems remain accurate, available, and governable over time — which is as important as building them in the first place.

Related Terms

Model Serving

The infrastructure that makes a trained model available to receive and respond to requests.

Model Drift

When a model's performance degrades over time as the real world changes.

Fine-tuning

Further training a pre-trained model on specific data to specialise it for a task.

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Disclaimer

This definition is provided for educational and informational purposes only. It represents a general explanation of a technical concept and does not constitute professional, technical, or investment advice. Artificial intelligence is a rapidly evolving field; terminology, techniques, and capabilities change frequently. Coaley Peak Ltd makes no warranty as to the accuracy, completeness, or currency of the information provided. Nothing on this page should be relied upon as the sole basis for commercial, technical, legal, or investment decisions without independent professional advice.

Document reference: ISO_webpage_knowledge-base_glossary_v1

Last modified: 29 March 2026

Knowledge Base·Infrastructure & Deployment·MLOps