Computer vision is already transforming manufacturing processes, with numerous applications such as quality inspection, safety monitoring, defect detection, supply chain management, and more. Computer vision-based solutions deliver the intelligence to simplify processes, drive new efficiencies, and empower faster decision-making.
However, AI developers are still facing challenges in the development and deployment of such solutions. The inability to run real-time inference, high false alarms due to low model accuracy, and difficulty in deploying on CPU or edge devices are just some of the barriers to production faced by AI teams today.
Learn how to boost your models’ performance and maximize hardware utilization to deliver accurate and cost-efficient inference on the cloud or edge devices. Download the case study of a publicly traded manufacturing company that was able to increase the model throughput by 1.6x, reduce the model size by 62%, and lower the memory footprint by 13%, resulting in better production efficiency and higher profitability.
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