AIOps Solutions is a UK-based practice for organisations that have tried — and stalled — on AI. We embed with your engineers, ship the first model that pays for itself, and train the people who’ll run it after we leave.
We help organisations frame, ship and operate machine-learning systems. When your team is light on specialists, we embed senior engineers alongside them; when they need the skills themselves, we train them in cohorts or in-house.
Every engagement moves through these four gates. Each gate has an artefact your board can read and your engineers can operate.
One workshop, one decision: which problem is worth a model, and which is better solved with a rule. We leave with a signed one-pager.
A time-boxed pilot on your real data. Success criteria set in advance; we walk away if the numbers don’t clear the bar.
The model gets a home — CI, monitoring, drift alarms, runbooks. Your engineers ship it, not us.
We train the on-call team, transfer the artefacts, and step out. A 90-day check-in is included; a retainer is optional.
Every course finishes with a portfolio artefact you can show a hiring manager and a runnable notebook you can drop into your team’s repo.
From messy CSV to a defensible answer. Pandas, statistical tests, honest charts, and how to present findings to a non-technical stakeholder without waffling.
Build, evaluate and interpret classification & regression models. scikit-learn, XGBoost, calibrated probabilities, and why your accuracy metric is lying to you.
Take a model from a notebook to a monitored, versioned, retraining production service. Docker, MLflow, GitHub Actions, drift & incident response.
The three profiles below are practitioners whose careers moved after a cohort. On the consulting side, our clients ask that we don’t name them — the Note from a client in the hero, and the Insights below, are representative of that work.
My fascination with AI led me to an MSc in Data Science despite not having a Computer Science background. Dee’s teaching made that transition possible.
Dee made Data Science simple. His teaching also made it easy for me to pass my Azure Data Scientist certification on the first attempt.
As a digital leader, understanding AI was paramount. Dee’s teaching on Data Science concepts made the ideas click faster than any amount of Googling could.
Short pieces from live engagements. No thought-leadership, no LinkedIn broetry — just what we’ve learned that we’d rather you didn’t have to.
Most enterprise AI programmes don’t die from bad models. They die from data ownership disputes, on-call fatigue, and a slow drift in what the business thinks the model is for. Here is how we structure the year-two review to catch each one early.
A short account of the interview format we replaced them with, and what it revealed about the candidates the old process was quietly filtering out.
Three real examples — redacted — of the one-page model summary we now include in every engagement handover.
We reply to every enquiry within one business day — from a real person, in Peterlee. If your project isn’t a fit, we’ll say so and point you to someone better placed.