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Page 10 - Ai Robogenius
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    OVERVIEW OF STAGES OF AI PROJECT CYCLE

                      By defining the problem, gathering relevant data, analysing it, building a model and evaluating its
                      performance, the AI project cycle helps ensure your project meets its objectives. This cycle offers
                      a structured roadmap for creating effective AI solutions.
                      The following figure shows the stages of AI Project Cycle:

                                                                    Problem
                                                                    Scoping

                                                                                       Data
                                                 Deployment
                                                                                    Acquisition

                                                                                       Data
                                                 Evaluation
                                                                                   Exploration

                                                                   Modelling

                      The description of the stages of AI project cycle is as follows:

                         Problem Scoping: This is the first step where the problem is identified and clearly defined using
                         the 4Ws—Who, What, Where and Why. It helps in understanding the issue and planning how AI
                         can solve it.
                         Data Acquisition: In this stage, relevant raw data (text, images, videos, etc.) is collected from
                         sources like the Internet, books or surveys to support the project.

                         Data Exploration:  The collected  data  is  analysed  and  visualised  using  statistical  tools  to
                         understand patterns, trends and insights.
                         Modelling: Suitable algorithms are selected to build and test models that can solve the problem
                         effectively. Different models are compared to choose the best fit.
                         Evaluation: The model is tested to check its performance. If it fails to meet the goals, data or
                         the model may be adjusted. Once ready, the model proceeds to deployment.

                         Deployment: The final model  is  implemented in a real-world  environment  with ongoing
                         monitoring and maintenance to ensure continued success.
                      These stages help in systematically address challenges from the initial problem identification.
                      It ensures that data collected is relevant and complete, which contributes to building a reliable
                      model and gaining meaningful insights.
                      By following the AI project cycle, you increase the chances of developing AI solutions that are not
                      only functional but also practical and impactful in real-world scenarios.

                                     AI RoboGenius - VIII
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