![]() ![]() To meet the computational requirements of the DNN models and deliver real-time inference performance to their end-users, Let’s Enhance chose Google Cloud A2 VMs powered by NVIDIA A100 Tensor Core GPUs as their compute infrastructure. Improved throughput and lower costs with NVIDIA A100 Multi-Instance GPUs (MIG) on Google Cloud Inference serving: NVIDIA Triton Inference Server Infrastructure management: Google Kubernetes Engine (GKE) There were three main components in the solution stack:Ĭompute resources: A2 VMs, powered by NVIDIA A100 Tensor Core GPUs Together, Google Cloud and NVIDIA technologies provided all the elements that the Let’s Enhance team needed to set themselves up for growth and scale. The key inference performance metrics to optimize for included latency (the time it takes from providing an input image to the enhanced image being available) and throughput ( the number of images that can be processed per second). While building and training these DNN models in itself is a complex, iterative process, application performance - when processing new user-requests, for instance - is crucial to delivering a quality end user experience and reducing total deployment costs.Ī single inference or processing request i.e., from user-generated image, which can vary widely in size, at the input to the AI-enhanced output image, requires combining multiple DNN models within an end-to-end pipeline. To deliver the high-quality enhanced images that end-customers see, Let’s Enhance products are powered by cutting-edge deep neural networks (DNNs) that are both compute- and memory-intensive. Architecting a solution to fuel the next wave of growth and innovation ![]() But before diving into their solution, let’s take a close look at the technical challenges they faced in meeting their business goals. To support their growing user-base, Let’s Enhance chose to deploy their AI-powered platform to production on Google Cloud and NVIDIA. It demands an infrastructure that’s easy to manage and monitor, can deliver real-time performance to end customers wherever they are and can scale as user-demand peaks, all while optimizing costs. However, building and deploying an AI-enabled service at scale for global use is a huge technical challenge that spans model building, training, inference serving and resource scaling. With the introduction of Claid.ai, their new API to automatically enhance and optimize user-generated content at scale for digital marketplaces, they needed to process millions of images every month and manage sudden peaks in user demand. To date, Let’s Enhance has processed more than 100M photos for millions of customers worldwide for use-cases ranging from digital art galleries, real estate agencies, digital printing, ecommerce and online marketplaces. “Let’s Enhance.io is designed to be a simple platform that brings AI-powered visual technologies to everyone - from marketers and entrepreneurs to photographers and designers,” said Sofi Shvets, CEO and Co-founder of Let’s Enhance. The Let’s Enhance platform improves the quality of any user-generated photo automatically using AI-based features to enhance images through automatic upscaling, pixelation and blur fixes, color and low-light correction, and removing compression artifacts - all with a single click and no professional equipment or photo-editing chops. That’s exactly the problem that Let’s Enhance, a computer vision startup with teams across the US and Ukraine, set out to solve with AI. The challenge? These user-generated images are often captured using mobile cameras and vary greatly in terms of their size, quality, compression ratios and resolution, making it difficult for companies to provide consistent high-quality product images on their platforms. In addition, there’s been a rapid shift towards user-generated visual content for ecommerce - think seller-generated product imagery, host-generated rental property photos and influencer-generated social media content. ![]() On e-commerce platforms and online marketplaces for example, product images and visuals heavily influence the consumer’s perception, decision making and ultimately conversion rates. There’s an explosion in the number of digital images generated and used for both personal and business needs. ![]()
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