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Our customer is a leading Web3 fan engagement platform catering to movie enthusiasts. The platform empowers fans to become super fans by facilitating interactions with forums, communities, studios, and celebrities. Offering a wide range of features including customizable collectibles, augmented reality (AR), artificial intelligence (AI), and deep tech-led utilities. Aimed to develop a cutting-edge face swap application that utilized computer vision and deep learning technologies to detect and replace faces in images and videos. However, this required high-performance computing to process large amounts of image data in real-time. Additionally, our customer wanted to ensure seamless integration with their existing platform, scalability to handle increasing user demand, and cost-efficiency in resource management. They needed a cloud solution that could meet these requirements and provide the necessary computational power.
Quadra, a trusted partner of Google Cloud, collaborated closely with them to deploy their face swap application on Google Cloud using Tesla T4 GPU VMs. Quadra’s expertise in cloud solutions and deep knowledge of Google Cloud's offerings allowed them to provide tailored guidance and support throughout the deployment process. The team worked on optimizing performance, configuring the infrastructure for scalability, and ensuring seamless integration with our customers' existing platform. By leveraging Google Compute Engine, our customer could achieve the desired performance and scalability while effectively managing their resources.
Quadra, along with the power of Google Cloud's Tesla T4 GPU VMs, brought about significant results for the face swap application. The real-time processing capabilities of the Tesla T4 GPUs enabled our customer to achieve faster and more accurate face detection and swapping, enhancing the overall user experience. The scalability provided by Google Compute Engine allowed our customer to handle increasing user demand and maintain optimal performance even during peak usage periods. With cost-effective resource management, they could efficiently allocate resources, ensuring cost-efficiency without compromising on performance.