I am trying to build a face recognition app but it must be accessible to other people with some type of URL link (web based GUI). I have attached the PDF of my requirement, in the PDF, I have a github that I can be used for my own project. There is a part A & B
Data product Github: https://github.com/KhomZ/Face-Recognition-Based-Student-Attendance-System-in-Python-using- OpenCV-with-Tkinter-GUI- Part A Due ASAP 1. Overview: Identifying the purpose, business context, and an overview of activities necessary to get the product into production, such as installation, configuration, and initial operation. Make sure to include a brief explanation of the cloud computing technologies and service platforms best used for this data product. 2. Assumptions, Dependencies, Constraints: Describe factors that can affect the deployment. 3. Operational Readiness: Identify the methods, preparations, or deviations from the original plans that will be used in assessing deployment readiness. 4. Data Conversion: Briefly describe the work required to populate, transfer, and validate the initial data. 5. Phased Rollout: Describe the methods and tasks required to phase in functionality and users. 6. Support: Present plans for training and aid for the use by all stakeholders. 7. Release Planning: Describe the methods, tasks, and contingency plans supporting the release of the product. Next, review the course Resources. Research and choose the most appropriate cloud-based platform to host your product. Your completed product must be accessible via a website. Ensure that all functions tested on a local computer are also working on the web. To show product deployment, present A URL where one can access the data product. The product must be hosted on a website that allows the instructor to test it. It could be a web-hosting account, shiny.io, or a cloud service provider. Park B Due NOV 15th: Reports and Information Visualization In a 1,000 – 1,250-word report using screenshots present this information. While each data product is unique, your reports and visualizations should include the following elements. 1. Quantitative data exploration reports. Provide descriptive statistics appropriate for the nature, type, and size of your data. 2. On-screen data visualization and its exploratory characteristics. Provide visual representations of all descriptive statistics described in the previous step. 3. On-screen data analytics. Provide detailed information about the results of your analysis, their meaning, their purpose, and why the particular analytics methods used produced the results needed. 4. Plots and graphs. Explain the choice of the plots and graphs. How can the user modify the characteristics and type of plots produced? What have you done to optimize the type and amount of information included in the plots? 5. Ability to export reports and plots to a file. Demonstrate how the reports can be saved in a file for future use or for dissemination via electronic communication. SUBMIT ASSIGNMENT https://github.com/KhomZ/Face-Recognition-Based-Student-Attendance-System-in-Python-using-OpenCV-with-Tkinter-GUI- https://github.com/KhomZ/Face-Recognition-Based-Student-Attendance-System-in-Python-using-OpenCV-with-Tkinter-GUI-