Machine learning (ML) is a way of building software that learns patterns from examples instead of following hand-written rules. Give it enough historical examples of an input and the right answer, and it learns to predict the answer for new inputs.
The three kinds of learning
- Supervised learning learns from labelled examples: past transactions marked fraud or not fraud, or last year's sales by week. Most business ML is supervised — forecasting, classification and scoring.
- Unsupervised learning finds structure without labels: customer segments, unusual sensor readings, groups of similar documents.
- Reinforcement learning learns by trial and feedback. It is powerful but less common in enterprise settings.
Large language models are trained with a mix of these techniques at enormous scale, but the same basic idea applies: learn from data, then generalise.
Common business problems
| Problem | Example | Typical approach |
|---|---|---|
| Forecasting | Demand, cash flow, staffing | Time-series models, gradient boosting |
| Classification | Churn risk, fraud, document type | Logistic regression, gradient boosting, neural networks |
| Ranking and recommendation | Next best product, search results | Collaborative filtering, learning to rank |
| Anomaly detection | Equipment faults, unusual payments | Isolation forests, statistical baselines |
Start with the decision, not the algorithm
The most important question is not which algorithm to use. It is: what decision will change, who makes it, and how will we know it improved? Define the baseline — the current process — and the cost of a wrong prediction before you build anything.
From notebook to production
Most models never reach production. The ones that do have:
- Reliable data pipelines feeding the same features in training and in production.
- A registry and CI/CD so models are versioned, tested and deployed like code.
- Monitoring for data drift and prediction quality, with alerts.
- Integration into the tool where the decision happens — not a separate dashboard nobody opens.
- An owner responsible for the model's performance over time.
Where to go next
If you are starting out, pick one decision with clear historical data and a measurable outcome, and build the simplest model that beats the baseline. Then invest in the pipeline around it.
Our machine learning practice helps teams do exactly that — and the data engineering underneath is usually where the real work is.
DataInn Team
Data, AI & Security practitioners
Written by DataInn's practitioners in Toronto and Dubai, who build and secure data platforms, AI agents and cloud systems for enterprise teams.
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