MLOps Consulting Services
Operationalise models with robust pipelines, monitoring and governance. MLOps and LLMOps from a maturity audit through to an operating retainer, for both traditional ML and generative AI.
Why models fail after launch
Getting a model live is one thing; keeping it accurate, observable and governed is another. MLOps is the discipline that stops production AI from quietly breaking.
We assess your current maturity, build the pipelines and monitoring you are missing, and can operate the result on a retainer, covering both traditional ML and generative AI (LLMOps).
- A maturity audit that shows exactly what is missing.
- Training, deployment and monitoring pipelines built to last.
- Covers both traditional ML and LLMOps.
The usual reasons
- Drift: the data the model sees in production changes, and accuracy drops quietly.
- No monitoring: nobody notices until a business user complains or numbers look wrong.
- Manual deployments: every update depends on one person and a set of scripts, so updates stop happening.
- No lineage: when something goes wrong, nobody can say which data and code produced the model.
MLOps consulting addresses all four with the right amount of tooling for the number of models you run.
LLMOps
Applications built on large language models need their own operating practices. Prompts change behaviour as much as code does, model providers update their models, and costs grow with every call. LLMOps covers versioning of prompts and configurations, evaluation of every change against a fixed test set, logging of inputs and outputs for review, and tracking of cost and latency per feature.
We set this up whether you use commercial APIs or open models in your own cloud, so you can switch models when a better or cheaper one appears without losing control of quality.
MLOps services
-
Maturity audit
A clear picture of your current MLOps practice and the highest-value gaps to close first.
-
CI/CD for models
Automated testing, packaging and deployment of models, with versioned data, code and configuration.
-
Model registry
One place for every model version, its training data, metrics and approval status.
-
Monitoring
Tracking of prediction quality, data drift, latency and cost, with alerts before users notice problems.
-
Retraining
Scheduled or triggered retraining pipelines that test a new model before it replaces the old one.
-
Governance and operation
Model documentation and an optional operating retainer so production AI stays accurate and accountable.
Frequently asked questions
What is MLOps?
The practices and tools that let you deploy, monitor and update machine learning models in a repeatable way, similar to DevOps for software.
Do we need MLOps for one model?
Even one model needs monitoring and a way to retrain. We size the setup to what you run.
Do you support LLM applications too?
Yes. LLMOps covers prompt versioning, evaluation, cost tracking and output logging.
Stop production AI from quietly breaking
Tell us where your models run today. We will audit your MLOps and close the gaps that threaten reliability.