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    After that, the system is trained using this data so that it can learn patterns and relationships.
                      Once trained, the system can make predictions or decisions when new data is given.
                      After building the system, it is tested to check whether it is working correctly. If there are errors,

                      the system is improved by using better data or making changes to the model. This process is
                      repeated until the system gives reliable and accurate results.
                      This complete process of planning, building, testing and improving an AI system is called the
                      AI project lifecycle. It helps in creating systems that are useful, accurate and able to improve
                      over time.

                                  The  TP-Link Tapo P110  Mini Smart  Wi Fi  Plug is  a  compact  and
                                 affordable  device  that  turns  regular appliances  into  smart  devices.
                                 It allows  users to  control appliances  remotely  and automate  daily
                                 tasks easily. It includes an energy-monitoring feature that helps track
                                electricity consumption and manage power usage efficiently. The device
                         ai  lens works smoothly with the Tapo app and other smart-home platforms.

                      AI PROJECT LIFECYCLE

                      The AI project cycle is a step-by-step guide used to plan, build and apply AI solutions. It provides
                      a clear structure for organising, developing and carrying out an AI project to achieve a specific
                      task. It helps in turning an idea into a working system that can be used in real situations and
                      maintained over time.

                      Following a proper AI project cycle ensures that each stage is carried out in an organised manner
                      and reduces the chances of errors or unexpected problems. The following figure shows the six
                      stages of the AI project cycle:

                      Defining the Problem                                                              fact bits
                                                                                                   The AI Project Cycle is
                      The first stage of an AI project cycle is defining the problem, where
                      the problem is clearly identified. This stage is important because a          based on CRISP-DM,
                      well-defined problem leads to a more effective AI solution. During               a data science
                      this  phase, the  focus is on understanding  the situation  in depth,        framework developed
                      identifying key challenges and determining how Artificial Intelligence              in 1996.
                      can be applied to address them.

                       12        Artificial Intelligence (CT & AI)-VIII

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