MLOps, LLMOps and AI Infrastructure
What happens after the model works. Ten engagements covering deployment, monitoring, evaluation, cost and infrastructure — the difference between a model that impressed a steering committee and one that runs.
The demo was never the hard part.
A model that works in a notebook has cleared roughly a third of the distance. The rest is whether anyone but its author can retrain it, whether a bad version can be rolled back before lunch, whether a silent accuracy collapse is noticed in a week rather than a quarter, and whether the monthly bill can be attributed to anything. None of that is model work, and all of it decides whether the model survives its first year.
So we build the least infrastructure that makes those things true, sized to the number of models you actually run and the skills of the people who will own it. The handover is measured by your team retraining and deploying something themselves while we watch — not by a document. Over-built MLOps platforms decay because nobody uses them enough to maintain them, and we would rather leave you a smaller system you actually run.
What we build
Each is a standalone engagement with its own scope, price and output. Most clients use two or three in sequence.
MLOps Pipeline Implementation
Versioned data and models, automated retraining, tested deployment and a rollback anyone on the team can run.
Read more →LLMOps and Prompt Versioning
Versioned prompts, an evaluation suite that gates releases, and cost, latency and quality tracked per change.
Read more →Model Deployment and Serving
Serving infrastructure sized to your real latency and throughput, with staged rollout and rehearsed rollback.
Read more →Model Monitoring and Drift Detection
Drift, delayed-label accuracy and segment-level performance, with alerts that lead to a decision.
Read more →AI Observability and Tracing
End-to-end tracing of every retrieval, prompt, tool call and token, so a bad answer can be explained.
Read more →CI/CD for Machine Learning
Automated testing of data, features and models, with evaluation gates that block a regression from shipping.
Read more →GPU Infrastructure and Cost Optimisation
Utilisation profiling, right-sizing, scheduling and purchasing strategy, measured before anything is changed.
Read more →Inference Optimisation and Latency Tuning
Profiling first, then quantisation, batching, caching and routing, each verified against an accuracy bar.
Read more →Edge AI Deployment
Models sized to real hardware, offline operation, safe over-the-air updates and fleet-wide monitoring.
Read more →Cloud AI Setup on AWS, Azure and GCP
A governed AI landing zone on AWS, Azure or GCP: identity, networking, residency, quota and cost guardrails.
Read more →How they fit together
You do not need all twelve. Most programmes follow one of these paths depending on where the uncertainty sits.
Operationalise trained models
MLOps pipeline implementation for reproducibility and automated retraining, CI/CD for machine learning so a regression cannot ship, and deployment and serving with a rehearsed rollback.
Operate language models and agents
LLMOps and prompt versioning with an evaluation suite that gates releases, and observability and tracing so a wrong answer can be explained rather than guessed at.
Know when it degrades
Model monitoring and drift detection reported by segment rather than in aggregate, with a defined response attached to every alert.
Control cost and infrastructure
GPU cost optimisation measured before it is changed, inference and latency tuning against an agreed accuracy floor, plus cloud AI setup and edge deployment.
FAQ
Marked up with FAQPage schema so these answers can surface in search results and inside AI assistant responses.
What is the difference between MLOps and LLMOps?
MLOps versions training data and model weights and measures accuracy against a labelled test set. LLMOps versions prompts and configuration, and correctness is often judged rather than computed because several answers can be acceptable. LLMOps also has to cope with a hosted model changing beneath you, which never happens with weights you trained and hold. Many organisations need both.
How much of this do we need?
Less than most vendors propose, and the sizing is part of the engagement. Two or three models with a monthly release cadence need versioning, an automated training pipeline and basic monitoring — not a platform. Elaborate infrastructure is justified by model count and release frequency, and building it early produces something that decays because nobody uses it enough to maintain it.
Our model has been in production for a year. Is it still working?
Without monitoring, nobody can answer that, and models fail silently — no error, no alert, just confident predictions that stopped being right. That is the case monitoring and drift detection exists for, and where prediction logging was never implemented, adding it is the first step because nothing unrecorded can be measured retrospectively.
How does this relate to data engineering?
This pillar assumes reliable data; it does not create it. Pipelines, governed storage and, where warranted, a feature store belong to data engineering and AI readiness. Building operational machinery on an unreliable data foundation produces a well-monitored pipeline that reliably delivers the wrong thing.
Do you operate this for us afterwards?
Only if you want us to. Everything is built so your own team can run it, and the handover is complete when they have retrained and deployed something themselves with us watching. Ongoing operation can be arranged separately, but it should be a choice rather than a dependency you did not notice acquiring.
Start with a conversation.
Thirty minutes, no charge, no deck. Tell us what you are trying to build with language models and we will tell you which of these engagements fits, or whether none of them do.