Machine Learning Development Services
Models that run in production and keep earning their place
What is Machine Learning Development?
A great many machine learning projects produce an accurate model and no business change. The model works in a notebook, the accuracy figure gets presented, and then it stalls - because nobody planned how it would be served, who would act on its output, or what happens when the data shifts six months later.
We build for the production end first. That means deciding early where predictions will be consumed, what latency the use case tolerates, how the model gets retrained, and how you will know when it has started to drift. The modelling itself is often the straightforward part; making it a dependable part of a daily workflow is the work that determines whether it delivers anything.
What We Deliver
Predictive Modelling
Forecasting for demand, churn, credit risk, maintenance windows and capacity planning. We start from the decision the forecast supports, which determines the accuracy actually required - frequently lower than assumed, and reachable far sooner.
Classification and Scoring
Models that categorise and rank - lead scoring, fraud detection, document routing, defect classification. Thresholds are tuned to the real cost of each error type, because a false positive and a false negative rarely cost the same.
Recommendation Systems
Collaborative filtering, content-based and hybrid approaches for product, content and next-best-action recommendations. Includes handling the cold-start problem for new users and items, which is where most naive implementations fail.
Natural Language Processing
Text classification, entity extraction, summarisation and semantic search using transformer models and, where appropriate, large language models. Applied to support tickets, contracts, reviews and internal documentation.
Computer Vision
Object detection, defect identification, OCR and video analytics for quality control, safety monitoring and document digitisation. Deployed to cloud or edge depending on latency and connectivity requirements.
MLOps and Monitoring
Versioned training pipelines, automated retraining, drift detection and performance alerting. Without this, accuracy degrades quietly and the first sign of trouble is a business metric moving the wrong way.
Problems This Solves
“A model that never reached production”
We focus on serving, integration and monitoring from the outset rather than treating deployment as a final step.
“Predictions nobody acts on”
Output is delivered into the tool where the decision is already made, not a separate dashboard someone must remember to open.
“Accuracy that degraded over time”
Drift detection and scheduled retraining catch the decay before it affects results.
“Not enough labelled data”
Transfer learning, synthetic data and active labelling strategies often make a project viable with far less labelled data than expected.
How We Work
Problem Framing
Define the decision, the cost of each error type, and what success means numerically.
Data and Baseline
Assess data readiness and establish a simple baseline to beat.
Model Development
Iterate on features and architecture, validating against held-out data.
Deploy and Monitor
Serve predictions, integrate into the workflow, and instrument for drift.
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NDA available upon request.