Tag: faang

Over the last 10 years, a number of players have developed autonomous vehicle (AV) systems using deep neural networks (DNNs). These systems have evolved from simple rule-based systems to Advanced Driver Assistance Systems (ADAS) and fully autonomous vehicles. These systems require petabytes of data and thousands of compute units (vCPUs and GPUs) to train. This …

Read more “Modular functions design for Advanced Driver Assistance Systems (ADAS) on AWS”

Posted by Hartmut Neven, VP of Engineering, and Julian Kelly, Director of Quantum Hardware, on behalf of the Google Quantum AI Team Many years from today, scientists will be able to use fault-tolerant quantum computers for large-scale computations with applications across science and industry. These quantum computers will be much bigger than today, consisting of …

Read more “Suppressing quantum errors by scaling a surface code logical qubit”

This post is co-written with Swagata Ashwani, Senior Data Scientist at Boomi. Boomi is an enterprise-level software as a service (SaaS) independent software vendor (ISV) that creates developer enablement tooling for software engineers. These tools integrate via API into Boomi’s core service offering. In this post, we discuss how Boomi used the bring-your-own-container (BYOC) approach …

Read more “Boomi uses BYOC on Amazon SageMaker Studio to scale custom Markov chain implementation”

Society is going cashless. While convenient for consumers, that’s caused a drastic decrease in income for tipped workers and this is the problem TackPay addresses. TackPay is a mobile platform that allows users to send, receive and manage tips in a completely digital way, providing tipped workers with a virtual tip jar that makes it …

Read more “No cash to tip? No problem. How TackPay built its digital tipping platform on Google Cloud”

Posted by John Platt, Distinguished Scientist, Google Research (This is Part 7 in our series of posts covering different topical areas of research at Google. You can find other posts in the series here.) It’s an incredibly exciting time to be a scientist. With the amazing advances in machine learning (ML) and quantum computing, we …

Read more “Google Research, 2022 & beyond: Natural sciences”

After you build, train, and evaluate your machine learning (ML) model to ensure it’s solving the intended business problem proposed, you want to deploy that model to enable decision-making in business operations. Models that support business-critical functions are deployed to a production environment where a model release strategy is put in place. Given the nature …

Read more “MLOps deployment best practices for real-time inference model serving endpoints with Amazon SageMaker”

In November 2022, we announced that AWS customers can generate images from text with Stable Diffusion models in Amazon SageMaker JumpStart. Stable Diffusion is a deep learning model that allows you to generate realistic, high-quality images and stunning art in just a few seconds. Although creating impressive images can find use in industries ranging from …

Read more “Fine-tune text-to-image Stable Diffusion models with Amazon SageMaker JumpStart”

Posted by Parker Riley, Software Engineer, and Jan Botha, Research Scientist, Google Research Many languages spoken worldwide cover numerous regional varieties (sometimes called dialects), such as Brazilian and European Portuguese or Mainland and Taiwan Mandarin Chinese. Although such varieties are often mutually intelligible to their speakers, there are still important differences. For example, the Brazilian …

Read more “FRMT: A Benchmark for Few-Shot Region-Aware Machine Translation”