Effective Data Science Infrastructure

8 Using and operating models

This chapter covers

• Using machine-learning models to produce predictions that benefit real-world applications
• Producing predictions as a batch workflow
• Producing predictions as a real-time application

Why do businesses invest in data science applications? “To produce models” isn’t an adequate answer, because models are just bundles of data and code with no intrinsic value. To produce tangible value, applications must have a positive impact on the surrounding world. For instance, a recommendation model is useless in isolation, but when connected to a user interface, it can lower customer churn and increase long-term revenue. Or a model predicting credit risk becomes valuable when connected to a decision-support dashboard ...