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These steps demonstrate how we plan out the tasks in our everyday life. Our mind creates plans for each activity that
            we must complete. Similarly, if we need to create an AI project, the AI model cycle offers us a suitable structure that can
            guide us towards our aim.
            In this unit, we will learn about the different stages of the AI model lifecycle. Next, we will learn how to select the correct
            AI algorithm for our model and understand the importance of an effective problem statement. At the end of this chapter,
             e  ill learn to integrate an image classifier on a  ebsite.


                   Stages of AI Model Lifecycle

            Generally, any AI model lifecycle consists of the following three phases:
            •  Phase I: Project planning and data collection

            •  Phase II:  Design and Testing the AI Model
            •  Phase III: Deployment and Maintenance at client site
            This is an iterative or cyclic process. Each phase is further divided into the following stages:




                           Problem Scoping           Data Acquisition         Data Exploration     Phase I





                             Deployment                Evaluation                Modelling         Phase II






                              Feedback           Phase III




            Let us understand each stage one-by-one.


            Phase I: Pr oj ect Planning  and Data C ollection
            Phase I is divided into three stages:  problem scoping, data acquisition and data exploration. Let us discuss about them
            in detail:


            Pr ob lem S coping
             efore beginning to build a solution, it is critical to first understand the problem description and business limitations.
            Business limitations assist you in realising the required solution's quality and terms. Consider the following two scenarios.
            Assume you are developing a version of “Google Translate”. You will think of the following:
            i.   What are the commercial constraints?
            ii.  Your model must be capable of comprehending text data. The end result should be linguistically correct as feasible.
            iii.  Minor mistakes are tolerable.
            iv.  For a better user experience, the result should be shown in milliseconds.
             o , compare it to an AI system that predicts the presence of a specific tumour based on    images.  o, ho  does  your
            current business constraints look like?





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