Machine Learning in Production
Machine learning creates value when it leaves the notebook and runs every day inside your operation. We design, train and deploy predictive and computer-vision models with MLOps from day one: versioning, drift monitoring, retraining and business metrics. In our manufacturing case study, a computer-vision model classifies microscopic defects with 98.7% precision.
What this service covers
What we build
- Demand, churn, delinquency or delivery-time prediction.
- Lead, risk or ticket-priority scoring.
- Computer vision for quality control, counting and inspection.
- Document classification and data extraction.
- Recommendation and routing systems.
- Fraud and anomaly detection.
MLOps from day one
- Versioned data and models, with reproducible experiments.
- Deployment in your cloud (Google Cloud, AWS or Azure) or at the edge, next to the line.
- Data and performance drift monitoring, with alerts.
- Scheduled or metric-triggered retraining.
- Business metrics next to technical ones: not just accuracy, but dollars and hours.
When machine learning pays off, and when it doesn't
If the rule can be written down, a rule is cheaper and easier to audit. Machine learning pays off when the pattern is too complex to write by hand, when there's enough historical data and when the cost of a mistake can be measured. We assess it in the discovery sprint with a cost model, before training anything.
Rules, machine learning or generative AI
| Criterion | Rules | Machine learning | Generative AI (LLMs) |
|---|---|---|---|
| Best for | Fixed, known logic | Predicting or classifying from historical data | Language, documents and conversation |
| Data needed | None | Labeled history | A few examples + your documentation |
| Cost per prediction | Very low | Low | Medium to high |
| Auditability | Full | High with good practices | Requires continuous evaluation |
Implementation process
- 1
Diagnostic
We analyze your process and tell you what to automate, how, and what return to expect.
- 2
Implementation
We build the solution connected to your systems, with working deliveries and real tests.
- 3
Operation and improvement
We leave it running, train your team, and measure results.
The full detail, with deliverables per stage, is in how we work.
Frequently asked questions
How much data do I need?
It depends on the problem. Tabular classification or prediction usually works with a few thousand historical records; computer vision needs hundreds to thousands of labeled images. We measure it in discovery before committing to results.
Where does the model run?
In your cloud or on your infrastructure. The code, the data and the trained model are yours.
What happens when the model starts to degrade?
Drift monitoring catches it and triggers an alert or a retraining run. The managed service includes that monitoring.
Have historical data and a decision you make thousands of times?
Tell us about the problem and we'll assess whether a model solves it better and cheaper than a rule.