Machine learning

Machine Learning is useful whenever learning from data is required rather than defining rules manually:

• Forecasting - sales, inventory, churn, energy demand, waste.
• Classification - automatically categorise documents, support tickets, prospects.
• Anomaly detection - fraud, industrial defect, suspicious user behaviour.
• Recommendation - products, content, commercial offers tailored to each user.
• Optimisation - logistics rounds, resource planning, dynamic pricing.

1. Verify the data - what volume, what quality, what freshness? Without clean data, no useful model.
2. Choose the right model type according to the problem (regression, classification, clustering, time series...).
3. Iterate - start with a simple model (often sufficient), then improve it if precision is insufficient.
4. Deploy to production - inference pipeline, integration into business tools, monitoring of model drift over time.

• Data quality - a poor dataset produces a poor model, regardless of the algorithm's sophistication.
• Bias - audit model biases (gender, origin, geography) before any client deployment.
• Maintenance - a model degrades when reality evolves. Plan for regular retraining and performance monitoring.