Deployment

 The Deployment section of the Data Science Methodology course emphasizes the crucial steps involved in making the data science model relevant and useful to stakeholders. Here's a summary of the key points covered:

Stakeholder Engagement: Stakeholders from various specialties, such as the solution owner, marketing, application developers, and IT administration, play a vital role in making the model's output relevant and useful. Their involvement ensures that the model addresses the initial question effectively.

Deployment Preparation: Before deploying the model, stakeholders assimilate knowledge to understand and translate the model results into actionable insights. In a healthcare scenario, for example, clinical staff need to understand how to identify high-risk patients and design suitable intervention actions to reduce readmission risk.

Business Requirements: During the business requirements stage, stakeholders specify their needs for the model, such as automated, near real-time risk assessments of congestive heart failure patients. The application should be easy to use, preferably browser-based on a tablet for clinical staff convenience.

Data Preparation and Model Application: Patient data generated during hospital stays is automatically prepared in a format needed by the model. Each patient is scored near discharge, providing clinicians with up-to-date risk assessments to select patients for intervention after discharge.

Training and Monitoring: The Intervention team develops and delivers training for clinical staff on using the model. Processes for tracking and monitoring patients receiving interventions are developed in collaboration with IT developers and database administrators to refine the model over time.

Example Deployment: An example deployment is provided through a Cognos application for hospitalization risk prediction in patients with juvenile diabetes. Decision tree classification is used to create a risk model, providing an interactive analysis of predicted risk nationwide, subgroup risk analysis, and detailed patient summaries for clinicians.

Overall, the Deployment section emphasizes the importance of stakeholder engagement, clear business requirements, effective model application, and ongoing monitoring and refinement to ensure the successful deployment and utilization of data science solutions.