90% reduction in process time compared to legacy solution
70% reduction in manual tagging efforts
High scalability to large numbers and complex brand logos
The client owns the leading marketplace platform for pre-owned and new luxury retailers to source and sell authentic, pre-owned luxury products, apparel, and accessories. Their online wholesale platform is the largest and most trusted platform for businesses to connect with global suppliers. However, with the rapid growth of the B2B wholesale unit, operational inefficiencies could be foreseen. They had a model for entity extraction of a title from an item and wanted to utilize an ML-based brand classification model to identify different brand styles.
Amazon Rekognition
Amazon SageMaker
AWS Lambda
Amazon S3
Python
Quantiphi developed a machine learning-based Image Classification Model that was trained on a dataset containing thousands of images on the AWS cloud stack. The solution identified the style, size, print, and material across ten different products of a brand.
Our team also presented a comparison between Rekognition and SageMaker in terms of accuracy, cost, time taken, and parallelism for the client’s use case.
