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AI Project Evaluation


            Evaluation is seen as the end of the Project cycle. It is an important step where the AI model is evaluated for
            its efficiency and accuracy. It enables continuous improvement of the model to achieve the project goals. The
            model must be tested with varied data to ensure that the results are satisfactory. Model is tested with the tested
            data after each stage of the AI project cycle. Final evaluation must be done to check the overall functioning of
            the model. Once the model is evaluated it must be deployed.

                    AI Project Deployment


            Deployment is the process of integrating a newly created AI model into an existing production environment to
            make practical implementation of the model with actual data taken as input to give the desired output.
            It requires certain settings to be done in terms of hardware and software so that the AI model can be put to use
            efficiently by the end users.

                                                                                                   Subject Enrichment

                        Video Session

                  Watch the video given below "Deploy and fine-tune large AI models with your data".
                  Visit:  https://www.youtube.com/watch?v=5y0xiHUKBW4 or scan the  QR code and
                  understand how the AI Project deploy and fine-tuned.







                      At a Glance


                  • AI project cycle is the process of converting the real-life problem into an AI-based model.
                  • Problem scoping means selecting a problem and finding a solution for it using AI technology.
                  • Data acquisition means collecting raw facts, figures or statistics from relevant sources either for reference or for
                 analysis needed for AI projects.
                  • Data is a piece of raw information or facts and statistics collected together for reference or analysis.
                  • The data on which we train our AI project model is called training data.
                  • Testing data is used to check the performance of an AI model.
                  • Data scraping is the method of downloading information from the World Wide Web (WWW) and storing it onto
                 your computer for later reference.
                  • A system map is a diagrammatic representation of a set of things working together.
                  • Data visualisation is the graphical representation of data and information.
                  • Data visualisation can be done through a combination of automated tools and manual methods.
                  • Modelling or data modelling is defined as the process of designing decision-making algorithms that has to be
                 trained on a set of data and apply that learning to recognise certain types of patterns.
                  • Rule-based approach is based on a set of rules and set of facts already fed to the machine to generate the desired
                 output.
                  • Learning-based approach refers to the model where the relationship or patterns in the data are not defined by the
                 developer.



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