AI Transformation · AI Product Engineering
Machine Learning Development
We build prediction, classification, forecasting and recommendation models on your data, compare them with a simple baseline, validate them on data they never saw in training, and explain each prediction to the people who act on it.
From question to model
Example questionWhich customers are likely to cancel in the next 90 days?
- 1FrameThe prediction, the decision it informs and how success is measured
- 2PreparePast records labelled with what happened, and features built from them
- 3TrainA simple baseline first, then candidate models compared with it
- 4ValidateOn recent data the model never saw in trainingTrainingValidationTestOlder recordsMost recent
- 5Explain and deployThe reasons behind each prediction, served through an API
How to get started
Three steps to a plan for Machine Learning Development
- 1Tell us what you needUse the project form or book a call. A few sentences about the goal is enough to start.
- 2Free technical consultationWe go through your goals, users, existing systems and constraints with you.
- 3Your planA detailed plan covering the right tech stack, architecture, timeline and budget. Then you decide.
Problem types
What a model can learn from your data
Choose a type to see the question it answers, what it produces and how it is measured.
“Will this transaction turn out to be fraud?”
Model output
Card not present, new device
- Amount
- $1,240
- Time
- 03:12
- Fraud probability
- 0.91
Hold for review
- It outputs
- A category, or the probability of each one.
- Measured by
- Precision, recall and ROC AUC, with the threshold set by the cost of each kind of mistake.
Validation
Results you can rely on
A model is only as good as the test it passed. These practices are part of every model we build, and their results are recorded in its model card.
- A held-back test setResults are reported on data kept out of training and tuning, so they show how the model will perform on new cases.
- Splits by timeWhen the past predicts the future, the model is trained on older records and tested on the most recent, as it will be used.
- A simple baselineEvery model is compared with a simple rule or model, so its added value is measured, not assumed.
- Leakage checksFeatures that would not be known at the moment of prediction are found and removed, before they flatter the results.
- Results by groupAccuracy is compared across customer groups, regions or products, so weak spots are found before launch.
- CalibrationA predicted 30% risk should come true about 30% of the time, so scores can be used to rank and decide.
- Error analysisThe cases the model gets wrong are reviewed with your experts, and often show what data is missing.
Explanations
Why the model predicted what it did
A churn model's prediction for three customers, broken down into the factors that raised or lowered each risk, with what a retention team can do about it.
Predicted churn risk: 39%
Factor
Month-to-month contract
Raises risk by 14 points
What it means
No term commitment, so the customer can leave at any time without a fee.
Retention action
Offer an annual plan at a lower monthly price.
Thresholds
Where to draw the line is a business decision
A fraud model's scores for a day of transactions. Move the threshold to trade missed fraud against reviews, and see what each costs.
False negative: Fraud that gets through, usually ending in a chargeback and fees.
Precision
5%
Recall
100%
Daily cost
$11,553
Lowest daily cost at 0.50: $2,396
Deliverables
What you receive
- A problem statement with its success measure
- Feature pipelines in your repository
- A baseline and the model comparison
- A validated model behind an API
- A model card: data, results, limits and intended use
- An explanation with each prediction
- Results by group, and the chosen threshold
- Monitoring for accuracy and drift
After the model
Where the work goes next
- AI Product EngineeringMLOps & AI OperationsKeep the model accurate in production, with monitoring, retraining and rollback.
- AI Product EngineeringIntelligent Process AutomationPut predictions to work inside a process, with people deciding the uncertain cases.
- Enterprise AI IntegrationData Foundations for AIBuild the pipelines and history the next models will need.
Questions
Questions about
Machine Learning Development
How much data do we need?
It depends on the problem. What matters most is enough past examples of the outcome you want to predict, including the rarer cases. We check this at the start and tell you if the data isn't enough, before a model is built.
Do you use deep learning?
Where the data calls for it, such as images, text and audio. For tabular business data, gradient-boosted trees often perform as well or better and are easier to explain, so we compare approaches against the same baseline and test set.
Can predictions be explained to customers and regulators?
Each prediction can come with the factors that pushed it up or down, calculated with SHAP values. The model card records the data used, the results by group and the model's known limits.
What happens when our data changes?
Accuracy and the data the model sees are monitored in production. When either crosses an agreed threshold, the model is retrained on recent data and re-validated before release.
How is a project priced?
Well-defined scopes are delivered as fixed-price engagements; when requirements are still evolving, we provide a dedicated team instead. Either way, the free technical consultation ends with a plan covering tech stack, architecture, timeline and budget, so you know the cost before work starts.
AI Product Engineering
Other services in this line
- AI Proof of Concept & MVPA small working version with agreed success criteria, to test value and feasibility before a full build.
- Generative AI DevelopmentAssistants, search and agents built on large language models, grounded in your own content and tested before release.
- Intelligent Process AutomationProcesses redesigned and automated with rules, AI and human approval at the steps that need judgement.
- MLOps & AI OperationsDeployment, monitoring, retraining and support for AI in production, with its cost and results tracked.
Ready to talk about Machine Learning Development?
Start with a free technical consultation: a plan covering the right approach, architecture, timeline and budget for your AI work.
Start a project