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•  Risk Management: Facilitates early identification and mitigation of risks related to data quality,
                model performance, deployment challenges, and ethical considerations, minimising potential
                disruptions.


                               •  Quality Assurance: Ensures rigorous testing, evaluation, and validation of AI models to meet
                              desired performance standards and business requirements, enhancing reliability and usability.


                 •  Continuous  Improvement: Supports iterative development and enhancement of AI solutions
                based on feedback, new data insights, and evolving business needs, fostering innovations, and
                adaptation over time.



                              •  Modularity: Encourages the design of AI solutions in a modular fashion, allowing components
                              to be developed, tested, and integrated independently, promoting flexibility and scalability in
                              project development.




                               Task                                             #Creativity & Innovativeness



                                               Case Study: Cleaning Oceans

                   The world's oceans are increasingly polluted with plastic waste, which poses a severe threat to marine life
                   and ecosystems. Identifying the location and density of plastic waste patches is challenging due to the
                   vast and dynamic nature of the ocean. Traditional methods of detecting and removing plastic waste are
                   inefficient and labour-intensive. Coastal communities and environmental organisations lack the advanced
                   tools  needed to monitor and clean up these plastics  effectively, leading to continued environmental
                   degradation and harm to marine biodiversity.
                   Can we solve this problem with AI? How?
                   Now that you are aware of AI concepts, plan to use them in accomplishing your task. Start with listing
                   down all the factors which you need to consider saving the oceans. This system aims to:






                     Problem Scoping: Define the problem statement, objectives, and constraints of the AI project. Identify
                     stakeholders, understand the business context, and outline the scope of work to be undertaken.

                   Define the scope to develop AI models for identifying and removing ocean plastic waste. Identify key
                   challenges such as ocean dynamics, plastic detection limitations, and scalability of cleanup efforts.

                     Data Acquisition: Gather relevant datasets required for training and evaluating AI models. Ensure data
                     is collected from reliable sources, adheres to legal and ethical standards, and is prepared in a format
                     suitable for analysis.

                   Gather  satellite imagery, drone footage,  and oceanographic data  from various sources to create a
                   comprehensive dataset.  Utilise  data  from environmental organisations, governments, and research
                   institutions.



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