Glossary: From Deployment to Feedback

 

Term	Definition
Browser-based application	An application that users access through a web browser, typically on a tablet or other mobile device, to provide easy access to the model's insights.
Cyclical methodology	An iterative approach to the data science process, where each stage informs and refines the subsequent stages.
Data collection refinement	The process of obtaining additional data elements or information to improve the model's performance.
Data science model	The result of data analysis and modeling that provides answers to specific questions or problems.
Feedback	The process of obtaining input and comments from users and stakeholders to refine and improve the data science model.
Model refinement	The process of adjusting and improving the data science model based on user feedback and real-world performance.
Redeployment	The process of implementing a refined model and intervention actions after incorporating feedback and improvements.
Review process	The systematic assessment and evaluation of the data science model's performance and impact.
Solution deployment	The process of implementing and integrating the data science model into the business or organizational workflow.
Solution owner	The individual or team responsible for overseeing the deployment and management of the data science solution.
Stakeholders	Individuals or groups with a vested interest in the data science model's outcome and its practical application, such as solution owners, marketing, application developers, and IT administration.
Storytelling	Storytelling is the art of conveying your message, or ideas through a narrative structure that engages, entertains, and resonates with the audience.
Test environment	A controlled setting where the data science model is evaluated and refined before full-scale implementation.