Running a closed feedback loop computer vision quality inspection application (Level 200)

Defect and anomaly detection in the quality inspection is a vital step to ensure the quality of the products, as timely detection of faults or defects and taking appropriate action often incur significant operational and quality-related costs. In addition, manual feedback loops are often subjective, time consuming and difficult to scale, resulting in production bottlenecks and slows down time to market. In this session, we share how you can build a robust, effective, and scalable closed loop quality inspection at the edge, generate objective decisions with the quick feedback loop and reduce quality related costs. Download slides »
Speaker: Derrick Choo, Solutions Architect, AWS

Previous Video
Breaking language barriers with AI (Level 200)
Breaking language barriers with AI (Level 200)

Amazon brings natural language processing, speech recognition, text to speech, and machine translation with...

Next Video
Intelligent media analytics with machine learning (Level 200)
Intelligent media analytics with machine learning (Level 200)

Media assets, such as audio and video, can be used to increase discoverability and drive greater user engag...