Every car that finds its way to a landfill marks another dent in the fight for a sustainable future. Reezocar, an online hub for buying and selling used cars, has a mission to change this.
Based in France, Reezocar offers a safe and streamlined process for purchasing refurbished vehicles and helps sellers fetch fair value for their cars. By using cutting-edge machine learning (ML) solutions, it provides buyers with detailed insights into a vehicle’s condition.
The technologies that underpin Reezocar’s efforts are part of Amazon Web Services (AWS). Using high performance computing (HPC) and machine learning (ML) infrastructure—powered by NVIDIA GPUs—on AWS, the company can meticulously detect car dents and imperfections. Its technology can estimate repair costs in milliseconds and helps extend the serviceable life of vehicles, steering them away from premature disposal.
Opportunity | Using AWS ML Services to Power Intensive Computer Vision Workloads for Reezocar
Reezocar has one of the largest catalogs of used and new cars in Europe, with millions of cars listed on its website at any given moment. Anyone in the world can visit Reezocar to look for their preferred car and have it delivered directly to their door. Users can also list their vehicles for sale on the website or request a trade-in from Reezocar.
“We want to make sure that existing cars on the market have the longest usable life possible,” says Laurent Fabre, chief technology officer at Reezocar. “We take these vehicles, estimate their repair needs, and determine the cost of refurbishing. After they are refurbished, we decide whether to sell direct to buyers or sell them for parts to professionals.”
Using ML and computer vision, Reezocar automatically calculates repair costs by analyzing images of each vehicle. The system meticulously scans photos for damages such as dents, scratches, and paint wear. Once identified, these imperfections are cross-referenced with a vast database containing average repair costs for similar damages on comparable vehicle models. Then, the system determines the estimated cost of repairing the vehicle.
As Reezocar grew, its initial cloud provider couldn’t handle the growing demand. The infrastructure was lackluster in performance, and Reezocar wasn’t receiving adequate technical support. The company needed a robust, responsive HPC infrastructure that could use ML to process these compute-intensive workloads and produce swift and accurate estimations.
Reezocar knew that it was time for a change, so it chose to migrate to AWS. “The customer obsession at AWS is real,” says Fabre. “We have never felt like we’re alone at all when we work with the AWS team. The level of professional commitment is unmatched.”
Estimating a vehicle’s repair costs usually takes around 90 milliseconds or less on AWS.”
Tarek Ben Charrada
Lead Data Scientist, Reezocar
Solution | Adopting Amazon EC2 P4d Instances to Estimate Repair Costs in 90 ms or Less
Reezocar began to explore AWS solutions by conducting a proof of concept. This initial step involved adopting Amazon Elastic Compute Cloud (Amazon EC2) P4d Instances, which are powered by NVIDIA A100 Tensor Core GPUs and deliver high performance for ML training and HPC applications in the cloud…