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Evaluation
                 The designed and tested model is now to be checked for its real-world
                 execution. The AI model is now given test cases and test data to check,
                 to see if it works accurately in real life and uncovers the errors that might
                 have been left hidden in the modelling and training phase. The trained
                 and tested model is then reformed based on the unprecedented outputs
                 that are encountered during evaluation phase. Sometimes the defined
                 parameters need tunings and adjustments to suit the problem in hand.


                                              Deployment
                                              After the modelling and evaluation phase, the AI model is deployed or implemented
                                              in real-life scenarios. The model is then integrated with existing systems. The new
                                              application is then utilised and upgraded as, and when some unprecedented
                                              scenarios cause some undesirable results. The model could be working dynamically
                                              in an online environment as well as offline, like reporting things to the manager.

                                              Understanding the nature of a problem provides insight into the components and
                                              attributes of  the  yet-to-be-implemented  solutions. A good  understanding  of  a
                 problem, guides future decisions to make at the later project stages, especially decisions such as determining if an AI
                 solution is even feasible. At the core of every AI model, is "finding patterns in data". If the data shows no patterns, then
                 most probably, the problem cannot be solved using AI.

                 A successful problem-defining process requires a basic analysis and evaluation of the project-related problems, their
                 reasons and methods. Finding the right problem definition is usually an iterative process. It can reveal more questions
                 and points to consider that would have been ignored without the problem definition process. The questions below
                 serve as a reference point for a thorough analysis of the problem and the problems that surround it. Just spending time
                 answering the following questions can save you weeks and months working on problems that proved impossible for
                 previously unknown reasons:
                 •   What is the problem that needs to be resolved?
                 •   Why do you need a solution to your problem?

                 •   How should we work on the solution to the problem?
                 •   Which aspects of the problem does the AI model solve?

                 •   How do I need to interact with the solution to the problem?
                 •   Which category of data will be involved? (Classification)
                 •   How much or how many? (Regression)

                 •   Can the data be grouped? (Clustering)
                 •   Is there any unusual pattern in the data? (Anomaly Detection)

                 •   Which option should be given to the customer? (Recommendation)
                 It is essential to determine which of these questions you’re asking and in what way answering them helps solve your
                 problem.







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