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Procedure
Trained technicians capture high-quality images of patients' eyes using specialised cameras.
The AI system analyses these digital images to identify the presence of Diabetic Retinopathy.
This approach accelerates the detection process and ensures timely diagnosis.
Technicians can operate the AI system without the need for a skilled doctor, making it accessible in rural areas.
Benefits
The AI-driven solution significantly benefits rural populations by enabling early detection and treatment of Diabetic
Retinopathy. This reduces the risk of severe vision loss.
#Digital Literacy
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 is deployed and fine-tuned.
Scan the QR code or visit the given link to watch the video, "Deploy and fine-tune large
AI models with your data"
https://www.youtube.com/watch?v=5y0xiHUKBW4
Scan the QR code or visit the given link to watch the video, "AI-Powered Pest
Management for Cotton Farmers"
https://www.youtube.com/watch?v=LP_A4jydmz4
AI Project Cycle Mapping Template
AI Project Cycle Mapping Template presents how different stages are related to each other and how the functions
performed in every phase forms an input for the next phase.
The performed task at one stage forms the root for the next stage.
AI Project: Customer churn prediction (identifying at-risk customers who are likely to cancel their subscriptions
or close/abandon their accounts.)
• Problem Scoping
✶ Identify the problem: The telecommunications company wants to reduce customer churn rates.
✶ Define objectives: Develop a predictive model to identify customers at risk of churning.
• Data Acquisition
✶ Gather data sources: Collect customer demographics, usage patterns, service history, and churn status data
from the company's databases.
✶ Ensure data quality: Clean the data, handle missing values, and remove duplicates.
• Data Exploration
✶ Explore the data: Analyse customer demographics, usage patterns, and churn rates through visualisations
and statistical summaries.
AI Reflection, Project Cycle and Ethics 219

