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Machine learning models on tabular data are often custom-made on company-specific data by machine learning experts. Models are trained on events like customers than churn or buy together with associated information like recent customer care contacts, size of contract, last purchases, etc.
AutoML services now support building machine learning models by just providing historic datasets, indicating what should be predicted and let the machine do the rest
Cross-industry services now support building machine learning models by just providing historic datasets, indicating what should be predicted and let the machine do the rest
Cluster data around norms
What to use it for
Use AutoML to predict if a customer will churn or respond to upsell
Use a purpose-built fraud detection model to identify fraudulent accounts and online payments
Forecast demand for a variety of related goods with deep time-series models
Detect anomalies within business metrics or in production data
Segment customers into groups with similar attributes