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The project identifies the area in which AI can be used to provide a solution.   Many times we are unable to
               observe any problem in our surroundings. In that case, we can take a look at the Sustainable Development
               Goals. Seventeen goals have been announced by the United Nations which are termed as the Sustainable
               Development Goals. The aim is to achieve these goals by the end of 2030.

            2.  Data Acquisition: The next stage of the AI project cycle is data acquisition. The term data acquisition means
               collecting raw data for the purpose of reference or analysis for the project. The data can be in the form of
               text, numbers, images, videos or audio. The data acquisition system allows us to obtain valuable information
               about reality to improve the performance of the project.

            3.  Data Exploration: Data exploration refers to the techniques and tools used to visualize data through complex
               statistical methods. It is the process of analyzing a large data set.
            4.  Modelling: It is the design phase of the project cycle. In this, we select the best way to reach the solution. It
               requires the process of selecting the right algorithm to develop a working model for the project. In this step,
               the algorithm is converted into a model.

            5.  Evaluation: This stage is the testing of the system, where we check if the model is capable of achieving
               required goals or not. If the model is not fulfilling the requirements, the model or even the data can be
               changed. Once the developer feels the project is ready, the project will be put into working conditions and
               then deployed and handed over to the user. If the deployment stage is not reached, the project is of no use.


                    Iterative Nature of Problem Scoping

            In the AI project cycle, problem scoping is a very important phase. If it is not handled properly and has flaws
            then it could lead to failure of the project as well.

            The iterative process is an important approach of problem scoping that helps in continually improving a design
            or product using an AI model. It involves creating a prototype and testing it, and repeating this cycle until you
            reach a desired AI model. The main advantages of using an iterative approach in problem scoping are:
               • Each iteration helps you improve based on the problems identified in the past cycle.

               • It is cost effective as the problem is identified and continual testing gives you a clear picture of the status of
              your project.
               • Testing and debugging are easier with smaller and initial iterations.
               • You can present the results of each iteration to stakeholders and clients and help you showcase the efficient
              progression of the project.


                    AI Ethics Practiced while Designing AI Projects


            Some of the AI ethics which 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.

               • Countercheck and validate all the assumptions taken and the results.






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