Demand forecasting
Predicting sales, inventory needs or capacity by product and location.
Technology / Foundations
Machine Learning
Machine learning is the engine behind most practical AI. It finds patterns in data and uses them to predict, classify, rank or detect. It is also the discipline where good data practice and honest evaluation matter most.
What is Machine Learning?
Short answer
Machine learning (ML) is a branch of artificial intelligence in which algorithms learn patterns from data, rather than being explicitly programmed with rules, so they can make predictions, classifications, recommendations or decisions about new data.
Machine learning comes in several forms. In supervised learning, models learn from examples with known answers, such as past transactions labelled as fraud or not. In unsupervised learning, models find structure in unlabelled data, such as customer segments or anomalies. Reinforcement learning trains systems through feedback on actions. Deep learning uses large neural networks and underpins modern vision, speech and language models.
For many business problems involving structured data, such as forecasting, churn or risk, classical methods like gradient boosting remain highly effective. The success of a machine learning project depends heavily on problem framing, data quality, feature design, realistic validation and ongoing monitoring.
Related concepts
- Supervised learning
- Deep learning
- Predictive analytics
- Feature engineering
- MLOps
- Data Engineering
- Model drift
How it works
How does Machine Learning work?
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Frame the problem
Define what to predict, for whom and how success will be measured.
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Prepare data
Collect, clean and transform data into features, and set aside test data.
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Train models
Algorithms adjust parameters to fit patterns in training data.
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Validate
Models are evaluated on unseen data, ideally reflecting how they will be used.
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Deploy and monitor
Models serve predictions in applications, and performance is tracked over time.
Applications
Where is Machine Learning useful?
Common applications across industries. Suitability always depends on the specific data, process and constraints.
Risk and fraud detection
Scoring transactions or applications for risk, and flagging anomalies.
Customer analytics
Churn prediction, segmentation and next-best-offer recommendations.
Predictive maintenance
Estimating equipment failure risk from sensor data.
Recommendation
Ranking products, content or actions for users.
Quality prediction
Predicting product or process quality from production parameters.
Limitations
Limitations and when not to use it
Every technology has boundaries. Knowing them early avoids expensive disappointment.
- Models reflect biases and gaps in historical data.
- Accuracy degrades when conditions change, known as drift.
- Rare events are hard to learn from limited examples.
- Correlations learned by models do not always reflect causes.
- Complex models can be hard to explain to users and regulators.
Common misconceptions
Deep learning is always best.
For tabular business data, simpler models often perform as well and are easier to maintain.
High test accuracy guarantees success.
Poor validation design, data leakage or changing conditions can make test results misleading.
Once trained, a model is finished.
Models need monitoring and retraining as data and behaviour change.
Comparison
Types of machine learning
Different learning approaches suit different kinds of data and problems.
| Type | How it learns | Typical business uses |
|---|---|---|
| Supervised learning | From labelled examples with known outcomes | Forecasting, classification, risk scoring |
| Unsupervised learning | Finds structure in unlabelled data | Segmentation, anomaly detection, topic discovery |
| Semi and self-supervised | Uses large unlabelled data plus few labels | Pre-training for vision and language models |
| Reinforcement learning | Learns from rewards for actions | Optimisation, control, recommendation tuning |
| Deep learning | Large neural networks, any of the above | Vision, speech, language, complex patterns |
Decision guide
How to evaluate Machine Learning for your organisation
Questions worth answering before investing. They form the basis of our feasibility and proof-of-concept work.
| Question to ask | Why it matters |
|---|---|
| What decision will the prediction improve? | A prediction without a use creates no value. |
| Is there enough relevant historical data with outcomes? | Supervised learning depends on labelled history. |
| What is the current baseline method? | Models must beat existing methods to justify investment. |
| Would validation reflect real use? | Time-based and group-based validation avoid misleading results. |
| Who will monitor and maintain models? | Unmonitored models degrade silently. |
AARHIT services
Services that apply Machine Learning
How we put this technology to work for organisations.
Hybrid Intelligence
Predictive Analytics
Forecast demand, risk, failure or behaviour with models that are validated, monitored and explained to users.
Explore service ResearchMachine Learning Research
Explore modelling approaches, features and training strategies for problems where the right method is not yet known.
Explore service Technology & EngineeringMachine Learning Operations
Version, deploy, monitor and retrain models with repeatable pipelines, so they stay accurate after launch.
Explore service Technology & EngineeringData Engineering and Analytics
Build pipelines, warehouses, lakehouses and analytics that give AI and reporting reliable, governed data.
Explore serviceRelated technologies
Industries
FAQ
Machine Learning FAQs
What is the difference between machine learning and deep learning?
Deep learning is a subset of machine learning using multi-layered neural networks. It excels with images, audio and text, while other machine learning methods often suit structured tabular data.
How much data does machine learning need?
It depends on the problem complexity and method. Some tasks work with a few thousand records; others need far more. Transfer learning and pre-trained models can reduce requirements.
What is model drift?
Model drift is the decline in model performance as real-world data or relationships change after training.
Can machine learning models be explained?
Many can, through inherently interpretable models or explanation techniques such as feature importance and SHAP values, though explanations for complex models are approximations.
How long does a machine learning project take?
A first model in use often takes 8 to 12 weeks, depending on data readiness, integration and evaluation requirements.
Next step
Exploring Machine Learning?
Tell us what you want to achieve. We will help you judge whether this technology is the right fit and how to test it with your own data.