What is machine learning?
Machine learning replaces hand-written rules with models trained on examples. Where a rule-based system needs someone to anticipate every case, a model learns the pattern from historical data and applies it to new cases: routing support tickets, reading invoices, spotting defects on a production line or flagging unusual transactions for review.
A model is only useful once it runs inside a business process. The goal of a machine learning engagement is therefore not a notebook with a good score. It is a model that is deployed, monitored, retrained when the world changes and trusted by the people who act on its output every day.
The scope covers the whole lifecycle: framing the problem, preparing data and features, training and validating, deploying and monitoring. It includes supervised learning, unsupervised methods such as clustering and anomaly detection, natural language processing and computer vision. Forecasting has its own page on predictive models, and all of it sits within our AI consulting practice.
Signs you need machine learning expertise
Machine learning is worth considering when a decision is repeated often, depends on patterns too complex for rules, and is backed by data. These are the situations in which teams usually call us.
A promising model never left the notebook
Data scientists built something that scores well offline, but nobody owns deployment, integration or monitoring, so it never reaches users.
Rules have become impossible to maintain
Hundreds of if-then rules contradict each other, and every new case means another exception added by hand.
Experts spend hours on repetitive judgments
Sorting documents, classifying requests or inspecting images takes specialist time that should go to the hard cases.
A model in production is quietly getting worse
Accuracy has drifted since launch, nobody measures it, and users have started to work around the output.
You must explain automated decisions
Auditors, regulators or customers ask why the model decided something, and the current system cannot answer.
The first sign is the one we meet most often. The gap between a working prototype and a production system is mostly engineering: data pipelines, packaging, serving, monitoring and clear ownership. Closing that gap is where an experienced team makes the largest difference, and where most internal projects lose momentum.
What our machine learning engagement covers
Each deliverable answers a question the business will ask sooner or later: what problem are we solving, how good is good enough, how does the model run, and who looks after it once the project team has moved on.
Problem framing and success metrics
The business decision the model supports, the baseline it must beat and the cost of each type of error, agreed before training starts.
Data pipeline and feature engineering
Reproducible preparation of training data, with features documented and the same logic used in training and in production.
Model training and validation
Candidate models compared against a simple baseline on held-out data, with results broken down by segment rather than averaged.
Deployment and integration
The model packaged, served in batch or in real time, and wired into the application or workflow where decisions are made.
Monitoring, retraining and handover
Data drift, model performance and business metrics tracked, a retraining routine defined, and your team trained to operate it.
We always compare the model with a simple baseline, such as the current rule or a basic statistical method. If the model does not beat it by a margin that matters to the business, we say so and recommend the simpler option, even when that ends the project early.
Model stuck in a notebook?
A discovery phase typically takes 2 to 4 weeks and ends with a framed problem, a data audit and a delivery plan.
How we deliver ML: four phases
The phases follow the model lifecycle, with a go or no-go decision after each one. A model that fails validation stops there, before any integration budget is spent on it.
PHASE 01
Frame
Business decision, success metric, baseline and data audit.
PHASE 02
Build
Features, training and comparison against the agreed baseline.
PHASE 03
Deploy
Packaging, serving, integration and a shadow run in production.
PHASE 04
Operate
Drift monitoring, retraining routine and team handover.
The shadow run is the step most teams skip. The model receives real production data and makes predictions that are logged but not acted on, so we can compare them with actual outcomes before anyone relies on them. It reveals the differences between training data and live data that offline validation cannot show.
Machine learning, rules or a pre-trained model?
Machine learning is not always the right tool. Before building anything, we compare it with the alternatives, because a simpler option that works is cheaper to run, easier to explain and easier to trust.
| Approach | When it fits | Strength | Limit |
|---|---|---|---|
| Business rules | Few cases, stable logic, full explainability required | Transparent and cheap to run | Breaks down as cases multiply |
| Pre-trained model or API | Common tasks such as text extraction or translation | Fast to deliver, no training data needed | Little control over behavior and cost per call |
| Custom supervised model | Labeled history and a decision specific to your business | Learns your patterns, with measurable accuracy | Needs data, monitoring and retraining |
| Unsupervised methods | No labels, but a need to group cases or find outliers | Works without labeling effort | Results need expert interpretation |
| Assisted human review | High error cost and low volume | Keeps judgment with experts | Does not remove the workload |
Many good systems combine approaches: rules for the clear cases, a model for the ambiguous middle and human review for the few cases where the model is unsure. Designing that split is part of the framing phase, and it is usually where the largest gains in user trust come from.
Risks in machine learning projects
Machine learning projects carry risks that ordinary software does not, because their behavior depends on data that keeps changing. These are the ones we plan for from the first week of the engagement.
Data leakage in training
Information from the future or from the target slips into the features, so validation looks excellent and production does not.
Drift after deployment
Customer behavior, products or upstream systems change, and accuracy erodes. Monitoring and a retraining trigger catch it.
Bias across groups
A model accurate on average can be much worse for one segment. We evaluate per segment and document the gaps.
No owner after launch
Models without an owner degrade silently. Handover names who watches the metrics and who decides to retrain.
These risks are manageable when they are visible. That is why every model we deliver comes with documentation of its training data, its intended use, its known limits and its evaluation by segment, in a form that your auditors and your engineers can both read.
Machine learning pricing
Every engagement starts with a diagnosis and ends with a fixed quote. Durations are typical, not promised.
| Format | Duration | Price |
|---|---|---|
| Discovery and data audit | 2 to 4 weeks | Fixed quote after diagnosis |
| Model build to production | 3 to 9 months | Fixed quote after the assessment |
| Monitoring and retraining | Monthly | Monthly fee quoted at handover |
What changes the price: data quality and labeling effort, number of models, real-time or batch serving, and depth of integration with your systems.
The quote covers senior consultants only, a defined scope and the deliverables of each phase, including model documentation and handover training. Compute for training and serving, labeling services and any licensed software are billed separately and estimated during discovery.
Machine learning FAQ
How much data do we need for machine learning?
It depends on the problem, not on a fixed threshold. A classification task with clear labels can work with modest history, while rare events need far more. The discovery data audit gives a concrete answer for your case.
Can you take over a model our team started?
Yes. We review the data, code and validation first, then decide with you whether to harden it for production or rebuild parts of it. Existing work is reused wherever it holds up.
Where will the model run?
In your environment whenever possible: your cloud account or your own servers. We choose batch or real-time serving according to how the business uses the prediction, and document the infrastructure it needs.
How do you make models explainable?
We choose interpretable models where accuracy allows, and add explanation methods for complex ones, showing which inputs drove each prediction. Documentation states what the model should and should not be used for.
What happens when the model’s accuracy drops?
Monitoring raises an alert when input data or performance moves beyond agreed thresholds. The retraining routine defined at handover then refreshes the model on recent data, and validation runs before the new version replaces the old.
Do you work with our data scientists?
Yes, and it is our preferred setup. We bring production engineering and the delivery method, your team brings domain knowledge, and by handover they can operate and extend the pipeline themselves.