Credit and risk scoring across consumer finance, real estate, insurance & reinsurance and financial markets is commonly done with linear models due to their reliability and interpretability. Whenever these models need to be integrated into an app, website, or internal service they need to be deployed to the web, usually as an API.

Sales and demand forecasts are commonly done via linear models, as these are often have good performance and high levels of reliability. Other common applications include marketing ROI modelling, pricing optimization and inventory management. If these need to be regularly done based on changing data, and done consistently across time and the organisation, deployment via API is your friend.

Customer segment classification, decision modelling and quantitative market modelling are generally done via linear models. Deploying these via API expands their effectiveness for customers.

Forecasting models, risk assessment models, macroeconomic models and many other kinds of models routinely produced in economic and policy consulting benefit from API deployment, which enables customers to evaluate their situation using new data as it becomes available

Customer churn prediction, newsletter optimization, UI personalization and many other modelling tasks can be done using linear models. Once deployed via API, they can improve the user experience and increase company effectiveness.

Anomaly detection, threat scoring and fraud detection are common usecases for linear models. Through API deployment, these techniques can be applied consistently through an organization or product. diagonal.sh enables frequent updating of these models as the threat landscape evolves.

Content optimization and game play adaptation can be dynamically learned from user behavior using linear models, and quickly fed back into the game play experience via APIs.

Impact modelling and forecasting of more general (macroeconomic or ecological, for example) and more specific variables in international development is frequently done using linear models, as they guarantee reliability and interpretability. Deploying them via API can make these models useful and responsive to new data across the world.

Linear regressions and variants such as Ridge regression and Lasso are one of the two model types with first-class support on diagonal.sh

Logistic regressions for classification are the second model type with first-class support on diagonal.sh

Through the update logic, both linear and logistic regression models can be adapted to new data as it comes in, and enable online-learning versions of these algorithms

Through multiple online-learning linear regressions, contextual bandits can be implemented fairly straightforwardly on diagonal.sh