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# System prepended metadata

title: Overview of Machine Learning Systems

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# Overview of Machine Learning Systems

## When to Use Machine Learning
_Machine learning is an approach to (1) learn (2) complex patterns from (3) existing data and use these patterns to make (4) predictions on (5) unseen data._
1. Learn: the system has the capacity to learn
2. Complex patterns: there are patterns to learn, and they are complex
3. Existing data: data is available, or it’s possible to collect data
4. Predictions: it’s a predictive problem
5. Unseen data: unseen data shares patterns with the training data
6. It’s repetitive
7. The cost of wrong predictions is cheap
8. It’s at scale
9. The patterns are constantly changing

## Machine Learning Use Cases
- A search engine or a recommender system.
- Machine translation
- Assistants on mobile phone
- Fraud detection
- Price optimization
- Brand monitoring
- Health-care application

## Understanding Machine Learning Systems
### Machine Learning in Research Versus in Production


|  | Research | Production |
| -------- | -------- | -------- |
| Requirements     | State-of-the-art model performance on benchmark datasets     | Different stakeholders have different requirements     |
| Computational priority     | Fast training, high throughput     | Fast inference, low latency     |
| Data     | Static     | Constantly shifting     |
| Fairness     | Often not a focus     | Must be considered     |
| Interpretability     | Often not a focus     | Must be considered     |
### Machine Learning Systems Versus Traditional Software
