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• By deploying the AI solution, organisations may assess its success, productivity, and relevance in real-world scenarios.
                 • Deployment is required for end users to communicate with the AI solution.
                 • Deployment enables the AI solution to grow and be implemented in a variety of situations or places.
                 • Deployed AI solutions  can provide considerable social and financial advantages by increasing productivity,
                lowering costs, and efficiently tackling crucial concerns.
              The deployment process for AI models involves several key steps:

              1.  Validating and testing the AI model: This phase ensures the performance of the AI model in real-world
                 situations that meet the expectations. This involves evaluating the accuracy, performance, and reliability of the
                 AI model.

              2.  Integration with current systems: After testing and validating the AI model, it must be integrated with the
                 existing systems and infrastructure of the organisation by linking the AI model to data sources, APIs, and other
                 software systems.

              3.  Monitoring  and  maintaining  the  deployed  AI  model:  After  deployment,  it is necessary  to  track  the
                 performance of the AI model to guarantee it remains effective. Monitoring and maintaining the deployed
                 AI model includes various tasks, such as evaluating its performance, identifying and fixing any errors, and
                 upgrading the model if required.

              Some examples of successful AI projects that have been deployed in various industries include as self-driving cars,
              medical diagnosis systems, and chatbots.
              Case Study: Preventive Blindness

              Problem: Prevent Loss of Vision and Delay in Report Generation
              Diabetic Retinopathy (DR) is a severe complication of diabetes that affects the blood vessels in the retina. This
              condition can lead to blurred vision and eventually blindness if not detected and treated promptly. Given that
              approximately 537 million adults aged 20-79 are living with diabetes, addressing the timely detection of DR is
              crucial. However, the lack of qualified doctors and delays in generating medical reports exacerbate the risk of
              undiagnosed and untreated Diabetic Retinopathy.
              Symptoms

              An early sign of Diabetic Retinopathy is blurred vision. Below is a comparison:












                                              Normal Vision                 Blurred Vision
              Solution

              To tackle this challenge, Aravind Eye Hospital in India has implemented
              an AI-driven solution in partnership with Google. This initiative leverages
              advanced AI models to accurately and quickly detect Diabetic Retinopathy
              in patients.

              AI implementation for detecting diabetic retinopathy:
                 • Collaboration: The AI eye screening solution was developed in
                partnership with Google.

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