ML Development Services
Custom machine learning models trained, deployed and kept healthy - predictive, recommender and risk models, with the MLOps to run them in a regulated environment.
ML development is the modelling discipline: turning your data into models that predict, rank, score or detect, and the MLOps to keep them working after launch. If you want that capability wrapped into a product or agent, see AI development; if you want text, code or image generation, see generative AI.
Results from work we have shipped
An AI-driven solution that optimises medical sales representative routes and improves field efficiency, using deep learning over visit patterns to predict the best routes.
ML we build
Demand, churn, lifetime value and risk over your historical data, with the validation to trust the numbers.
Ranking and next-best-action models for products, content and operations.
Transaction and behavioural models with a feature store and real-time scoring. The applied fraud product lives on AI development; here we build and tune the models behind it.
Underwriting and risk models built to be validated, monitored and explained.
Models that cut travel time and raise visits per day for field and logistics operations.
Deployment, monitoring, drift detection, retraining and a model registry, so a model survives contact with production.
The data plumbing that decides model quality - built once, owned by your team.
How we deliver ML development
The same delivery discipline on every engagement - from the first map to a handover your team runs.
We check the data you have, its quality and its governance, because a small early investment there saves a large later one.
We build the feature engineering and the training data so results are reproducible.
We train, validate and challenge the model against the bar your use case needs, with the evidence written down.
Deployed in your cloud with drift detection, a retraining cadence and monitoring on the model's own targets, handed to your team.
What shapes the work
A model is only worth building if a decision changes because of it. We start from that decision, the data behind it and the bar it has to clear, then build the simplest model that clears the bar and the pipeline to keep it there.
In a regulated setting the modelling and the governance are one job. Where the data came from, how a prediction is logged, how the model is validated and challenged - these are designed in, not added when an auditor asks. Where the question is really analysis rather than a model, that is data analytics.
Most models degrade quietly. The data shifts, the world moves, and a model that was accurate at launch is wrong six months later without anyone noticing. So we treat monitoring, drift detection and retraining as part of the deliverable, not an afterthought - with a model registry and a cadence your team can run alone. Where the model needs live data, that work sits next to our API integration services.
We start with a data audit because the quality of the training data decides the quality of the model, more than the choice of algorithm does. If the data is not ready, a short, honest audit tells you that before you fund a full build. Where the goal is to understand the data rather than predict from it, our data analytics service is the better fit, and we will say so.
A proof of concept on your own anonymised data is fixed-price and time-boxed, with one question: does the model clear the bar. If it does, production follows as time and material or an outcome-based arrangement, with monitoring and retraining built in from the start. The full commercial approach is on the Data Science &
How is ML development different from AI development?
ML development is the modelling: training custom models, predictive and recommender systems, and the MLOps to run them. AI development wraps a capability into a product - agents, vision, NLP, decisioning and integration. Many engagements use both.
Do you do MLOps, or only build the model?
Both. Deployment, monitoring, drift detection, retraining and a model registry are part of the default handover, so your team can run the model without us.
What kinds of models do you build?
Predictive and forecasting models, recommender systems, anomaly and fraud models, credit-risk and scoring models, and optimisation models for operations.
Our data is messy. Can you still help?
We start with a data audit for exactly that reason. If the data is not ready, you learn it from a small fixed piece of work, not a large one.
How do you keep models explainable and compliant?
We pick model classes and logging that produce explanations by default, validate and challenge the model, and design monitoring and governance in from the start.
Who owns the model and the pipeline at the end?
You do - source, feature pipelines, training scripts, model artefacts and documentation, running in your cloud.
This was a very task-heavy project, mostly exploration and R&D-driven. However, by the end of WislaCode, we were left with a detailed roadmap consisting of clear milestones - able to be converted into tangible KPIs - and some neat ideas of what actionable are next. Integrating...
We collaborated with WislaCode on a product strategy development project and gave the highest marks for this contractor. The WislaCode team delivered on time and with outstanding quality.
We collaborated with WislaCode on a route-to-market optimisation project. Working with WislaCode was effective, transparent and predictable, which is especially critical for AI and ML projects. We provided them with six months of anonymised data, and within just three weeks...
Have a prediction worth making?
Bring the decision and the data behind it. We will audit the data and scope a proof of concept that answers whether a model clears your bar.