Machine learning & MLOps

Machine learning that survives contact with production, not just a notebook.

Most machine learning consulting services stop at a promising notebook and a slide of metrics. Our machine learning development services and MLOps take the model the rest of the way — deployed, monitored, and retrained automatically as your data shifts — with a first model live in production in as little as 5 weeks.

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Challenges we solve

The problems that bring
teams to us for machine learning.

If any of these sound familiar, another notebook isn't the fix — proper MLOps is. Here's how we approach it.

A model that only ever ran in a notebook

It scored well in testing eighteen months ago. Nobody's worked out how to actually ship it since.

Model performance quietly decays in production

Nobody's watching for drift, so accuracy erodes for months before anyone in the business notices.

No pipeline to retrain as the data changes

Every retrain is a manual, one-off project instead of something that just happens on schedule.

No clear way to explain a wrong prediction

When the model gets it wrong, nobody can say why — which makes it hard for anyone to actually trust it.

The High Digital approach

Built for production,
not just for testing.

Most machine learning consulting services stop at a notebook that scores well against a held-out test set. Ours starts with what happens after that — deployment, monitoring, retraining — because that's where a model either earns its keep or quietly stops working.

5 wks

From kickoff to a first model live in production

100%

Monitored — drift and performance tracked, not assumed

1 team

Data engineering and ML from the same senior team

8+ yrs

Experience building production data platforms

Why it matters

A model in a notebook has never made anyone money.

Plenty of machine learning projects prove the concept and stop there. Ours are built as production software from the start — deployed, monitored for drift, and retrained on a schedule — so the model keeps earning its place long after the first demo.

Machine learningMLOpsModel deploymentModel monitoringPredictive analytics

Built for production, not just a demo

A model that scores well in testing and a model that survives real traffic and messy data are different engineering problems — we build for the second.

Retraining is a pipeline, not a one-off

New data flows back into the model automatically, so performance doesn't quietly decay while everyone assumes it's fine.

Monitored, so drift gets caught early

Model performance and data drift are tracked continuously, not discovered three months later in a board meeting.

AI-accelerated, senior-led build

AI-assisted tooling speeds up the plumbing so senior engineers focus on the modelling decisions that actually move the metric.

How we work

A working method, not a deck of phases.

Discover

We get into the detail. Stakeholders, constraints, data, and the real problem you're trying to solve.

Strategise

We sketch the smallest version that proves the outcome. A clear plan, a tight scope, no fluff.

Build

Cross-functional pods of engineers, designers, and data folk. Working software every week.

Scale

We harden it, instrument it, and stick around. Roadmaps, reviews, and a team that knows your stack.

Not sure if you need a custom model or an off-the-shelf one? Ask the engineer who'd build it.

Book a 30-minute working session with a senior data engineer — a real conversation about your data, not a sales call.

Book a data review

The people behind the work

“A model that scores well in testing and a model that survives real production traffic are two different engineering problems. Most teams only budget for the first one.”

Amit Kumar

AI Strategist · High Digital

Meet Amit

Industries

Sectors we've deployed machine learning for.

Every sector has a prediction problem worth solving properly — we've taken models into production for teams across all of these.

01B2B Marketing 02Cold Chain Logistics 03Construction 04ESG & Sustainability 05Financial Services 06International Trade 07Manufacturing 08Professional Services

Tools

What we build with.

Chosen because they run reliably in production, not just in a research environment.

ML frameworks

PyTorch
scikit-learn
TensorFlow

MLOps & deployment

MLflow
Kubeflow
SageMaker

Cloud & infrastructure

AI-accelerated build

Claude
Cursor
GitHub Copilot

Oliver's adept project management skills were evident as he consistently delivered all projects within set timelines.

Ziada Tesfamichael

Business Development Manager · Think Employment

verified by

Machine learning consulting,
from model to production.

See all services

Overview

Data engineering

We design and build the data infrastructure that powers intelligent decision-making — from ingestion to insight.

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Data products

Standalone data products your team can actually use.

Learn more 02

Data solutions

Bespoke solutions to your most complex data challenges.

Learn more 03

BI & analytics

Dashboards and reporting tools that turn raw data into clear decisions.

Learn more 04

Machine learning & MLOps

Predictive models and ML pipelines designed for production.

Learn more 05

Data consultancy

Strategic advice on how to structure, govern, and get more value from your data.

Learn more

FAQ

Questions, answered straight.

What is MLOps, and why does it matter for production machine learning?

MLOps is the discipline of running machine learning like production software — deployment, monitoring, retraining pipelines and version control — rather than a one-off data science project. Without it, a model that worked in testing quietly degrades in production and nobody notices until the numbers are visibly wrong.

Do you build custom models, or deploy and run existing ones?

Both, depending on the problem. We build custom models where your data and use case are specific enough to need one, and integrate off-the-shelf or pre-trained models where that's genuinely the faster, more sensible route — we'll tell you which fits before you commit budget.

How do you monitor and maintain models once they’re live?

Every model we deploy gets monitoring for performance and data drift, with alerting when accuracy drops below an agreed threshold. Retraining is built as a repeatable pipeline, not a one-off event, so the model keeps working as your data changes.

What’s the difference between ML consulting and ML as a service?

Our machine learning services can mean either. ML consulting is us building and deploying a model specific to your data and business problem — closer to mlops consulting services, hands-on and bespoke. Machine learning as a service usually means an off-the-shelf, pre-trained model or API you integrate rather than something custom-built — cheaper and faster where your use case is generic enough for it to fit.

How do you measure whether a model is delivering business value?

We agree the business metric the model's meant to move — conversion, cost saved, time reduced — before we start, and track that after deployment, not just the model's technical accuracy score. A model with great precision that doesn't move the business number isn't a win.

Let’s talk

Have an outcome in mind?
We'll help you
ship it.

You're building a data product and need a team that can deliver.
You want to get AI-ready — pragmatically, not theoretically.
Your reporting is a mess and you need a real platform underneath it.

Scope your project

call ·+44 (0)20 3633 5002

email ·info@highdigital.co.uk

Machine learning that survives contact with production, not just a notebook.