Welcome to Data Science Methodology 101 From Deployment to Feedback - Deployment! While a data science model will provide an answer, the key to making the answer relevant and useful to address the initial question, involves getting the stakeholders familiar with the tool produced. In a business scenario, stakeholders have different specialties that will help make this happen, such as the solution owner, marketing, application developers, and IT administration. Once the model is evaluated and the data scientist is confident it will work, it is deployed and put to the ultimate test. Depending on the purpose of the model, it may be rolled out to a limited group of users or in a test environment, to build up confidence in applying the outcome for use across the board. So now, let's look at the case study related to applying Deployment" In preparation for solution deployment, the next step was to assimilate the knowledge for the business group who would be designing and managing the intervention program to reduce readmission risk. In this scenario, the business people translated the model results so that the clinical staff could understand how to identify high-risk patients and design suitable intervention actions. The goal, of course, was to reduce the likelihood that these patients would be readmitted within 30 days after discharge. During the business requirements stage, the Intervention Program Director and her team had wanted an application that would provide automated, near real-time risk assessments of congestive heart failure. It also had to be easy for clinical staff to use, and preferably through browser-based application on a tablet, that each staff member could carry around. This patient data was generated throughout the hospital stay. It would be automatically prepared in a format needed by the model and each patient would be scored near the time of discharge. Clinicians would then have the most up-to-date risk assessment for each patient, helping them to select which patients to target for intervention after discharge. As part of solution deployment, the Intervention team would develop and deliver training for the clinical staff. Also, processes for tracking and monitoring patients receiving the intervention would have to be developed in collaboration with IT developers and database administrators, so that the results could go through the feedback stage and the model could be refined over time. This map is an example of a solution deployed through a Cognos application. In this case, the case study was hospitalization risk for patients with juvenile diabetes. Like the congestive heart failure use case, this one used decision tree classification to create a risk model that would serve as the foundation for this application. The map gives an overview of hospitalization risk nationwide, with an interactive analysis of predicted risk by a variety of patient conditions and other characteristics. This slide shows an interactive summary report of risk by patient population within a given node of the model, so that clinicians could understand the combination of conditions for this subgroup of patients. And this report gives a detailed summary on an individual patient, including the patient's predicted risk and details about the clinical history, giving a concise summary for the doctor. This ends the Deployment section of this course. Thanks for watching!