Streamlining access to powerful disaster recovery capabilities of AWS

A serverless, event-driven architecture using computer vision and generative AI is developed to automate safety monitoring across hundreds of facilities. The system captures near real-time visual data from fixed camera networks, detects potential safety hazards, and scales across thousands of cameras, while protecting personal identifiable information (PII) by blurring faces and identifiable features. The architecture is designed to learn and improve detection accuracy continuously, using a hierarchical role-based access control structure and synthetic data generation with generative AI tools like GLIGEN to reduce site onboarding time and data labeling requirements.

Aigen, a company specializing in agricultural robotics, transformed its operation with Amazon SageMaker AI to develop sustainable farming solutions. They replaced their on-premises infrastructure, which bottlenecked their model-building pipeline, by adopting AWS AI-driven and cloud-native approach. This led to significant improvements in image labeling throughput and costs, achieving 20x faster labeling and 22.5x cost reduction. Aigen's cloud-based pipeline overcame connectivity issues, high labeling costs, and limited computational power challenges by using automated data pipelines and leveraging cloud-native machine learning capabilities. Their solution utilizes edge computing, a data annotation process that combines automated and human validation, and cloud-based training and inference processes.