Machine Learning

Which customers will churn in the next 90 days? Which invoices will be paid late? Which parts will fail next? The answers are in your data. Machine learning (ML) makes them usable — algorithms that detect patterns and make predictions without anyone programming every decision rule by hand.

Supervised, unsupervised, reinforcement

Supervised learning trains on labeled data: input plus known outcome. Spam detection, credit scoring, price forecasting. Unsupervised learning works without labels and finds structure on its own — typical for customer segmentation and anomaly detection.

Reinforcement learning works differently. An agent learns through trial and error, receiving rewards for good decisions. Behind robotics, autonomous driving, and RLHF — the training technique for ChatGPT.

When ML, when rules?

Rule-based systems follow explicit if-then logic. Work for clear, stable conditions. ML learns the rules from data — better suited for complex, shifting patterns. The trade-off: less transparency. A decision tree is interpretable. A neural network with 100 layers is not.

Where to start

71% of the ML market ($48-94 billion, 2025) runs on cloud-based services. SAP, Microsoft, and AWS lead in the enterprise segment. The simplest entry point: solve a classification problem with existing data. One model, one dataset, one measurable result.