Overcoming Increased Cost and Data Aggregation Issues

Challenges

• Collecting and aggregating data from drone feeds posed a significant hurdle.
• Creating a data pipeline capable of handling real-time data for instant predictions was a key objective.
• Efficiently performing inferences on multiple feeds simultaneously while minimizing GPU usage was a priority.

• Experts in our AI development and AI consulting services, proposed an interactive web portal with AI models, including maintenance prediction and analyzing solar panel dust, mineral deposition, and physical damage. The objective was to empower end clients to enhance the effectiveness of their plant maintenance.
• These AI models were designed to continuously learn and adapt even after deployment, ensuring real-time adaptation to changing scenarios. The maintenance prediction model, which considered over 40 parameters, aimed to provide highly accurate predictions of panel maintenance needs within a 14-day timeframe.