Aucnet is one of the largest real-time auction service providers in the world, handling four million auctions every year. But one of their pain points was image classification. Entering a single car to auction required uploading 20 photos from various angles with labels like "front view," "side view," "tire," "handle," "seat" and so on. This time consuming task could take up to 20 minutes per car.
But Aucnet had an idea. By training their existing IT engineers in the basics of ML, they were able to build Konpeki (click the link to try the demo yourself), a real-time car image recognition system, powered by TensorFlow. By integrating various deep learning technologies, ML APIs and Google Cloud services, they shortened the amount of time it took to list a car auction to just a few minutes, down from 20. Also, Aucnet was able to apply Cloud Machine Learning Engine to increase the speed of ML training process by 86 times.
Read more here
But Aucnet had an idea. By training their existing IT engineers in the basics of ML, they were able to build Konpeki (click the link to try the demo yourself), a real-time car image recognition system, powered by TensorFlow. By integrating various deep learning technologies, ML APIs and Google Cloud services, they shortened the amount of time it took to list a car auction to just a few minutes, down from 20. Also, Aucnet was able to apply Cloud Machine Learning Engine to increase the speed of ML training process by 86 times.
Read more here
