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• Deployment
                    ✶ Deploy model: Integrate the selected model into the company's customer management system to predict
                   churn risk for new customers.
                    ✶ Monitor performance: Monitor the model's predictions in real-time, track churn rates, and gather feedback
                   from customer service interactions.

              In this example, each phase of the AI project cycle builds upon the outputs of the previous phase:

                                                 AI Project Cycle Mapping Template

                                         Data            Data
                Problem Scoping                                        Modelling        Evaluation      Deployment
                                     Acquisition      Exploration

               The                 Gather customer  Analyse          Select machine   Evaluate        Integrate the
               telecommunications  demographics,    customer         learning        each model's     model to predict
               company wants to    usage patterns,   demographics,   algorithms for   performance     new customer
               reduce customer     service history,   usage patterns,   classification,   using accuracy,   churn risk.
               churn rates.        and churn        and churn        like logistic   precision, recall,
                                   status data      rates with       regression,     and F1-score.
                                   from company     visualisations   decision trees,
                                   databases.       and statistical   and random
                                                    summaries.       forests.
                 • Problem Scoping: States the problem that needs attention.

                 • Data Acquisition: Data acquisition consists of two words: Data and Acquisition. Data refers to the raw facts,
                figures, information, or statistics; where as, acquisition refers to acquiring data for the project.

                 • Data Exploration: It is the first step of data analysis that is used to visualise data. It generates insights that are
                used to inform modelling decisions.
                 • Modelling: Develops predictive models based on insights gained from data exploration.
                 • Evaluation: Assesses model performance by feeding the data into the model and comparing the output with
                the actual answers. It is used to determine the best model for deployment.
                 • Deployment: Integrates the selected model into the company's systems for real-world usage.

              The feedback loop continues as the deployed model's performance is monitored, and insights gathered are used
              to refine future iterations of the AI solution.


                       AI Ethics

              Ethics are rules about what is right and wrong. AI ethics are rules about using artificial intelligence (AI) in a good
              way. As the use of AI increasing, companies are creating rules called AI codes of ethics.

              An AI code of ethics is a set of rules that says how AI should be used to help people. These rules allow people make
              good decisions when using AI.
              Some of the AI ethics that you need to follow while working on an AI model are:

                 • Model should be understandable by all.
                 • Every aspect of the model should be self-explanatory and transparent.

                 • Covering all the sections of the population is important.

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