Machine Learning
Machine Learning engineers and Data Scientists work together closely to create usable products for clients. While there’s some overlap, Data Scientists focus on analysing data, providing business insights, and prototyping models, while Machine Learning engineers focus productising them by coding and deploying complex, large-scale machine learning products.
Use Cases
More and more companies are applying Data Science or Machine Learning (ML) to make predictions and expectations based on their data. They do this to be able to respond more quickly to possible changes that affect their business processes, or to maintain their competitive advantage. Some (well-known) examples are:
- Predictive Maintenance, performing risk-based maintenance on assets with a certain expected failure probability.
- Customer recommendations, cross-selling and/or upselling based on people with similar interests/purchases
- Fraude detection
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