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Posted by Dahun Kim and Weicheng Kuo, Research Scientists, Google The ability to detect objects in the visual world is crucial for computer vision and machine intelligence, enabling applications like adaptive autonomous agents and versatile shopping systems. However, modern object detectors are limited by the manual annotations of their training data, resulting in a vocabulary …
As part of the 2023 Data Science Conference (DSCO 23), AWS partnered with the Data Institute at the University of San Francisco (USF) to conduct a datathon. Participants, both high school and undergraduate students, competed on a data science project that focused on air quality and sustainability. The Data Institute at the USF aims to …
Planning and managing your cloud ecosystem and environments is critical for reducing production downtime and maintaining a functioning workload. In the “Managing your cloud ecosystems” blog series, we cover different strategies for ensuring that your setup functions smoothly with minimal downtime. To start things off, the first topic in this blog series is ensuring workload …
Posted by Susanna Ricco and Utsav Prabhu, co-leads, Perception Fairness Team, Google Research Google’s Responsible AI research is built on a foundation of collaboration — between teams with diverse backgrounds and expertise, between researchers and product developers, and ultimately with the community at large. The Perception Fairness team drives progress by combining deep subject-matter expertise …
Read more “Responsible AI at Google Research: Perception Fairness”
As enterprises race to extract value from structured, semi-structured, and unstructured data, they face a continuum of challenges related to data gravity, including data acquisition, data management and data governance. Simultaneously, these companies are also grappling with model gravity as they build and scale machine learning workflows for their predictive analytics needs. Core data warehousing …
Read more “Scaling machine learning with BigQuery ML inference engine”
For disaster-prone areas, fragile connectivity remains a major problem, often going offline in critical moments. Aerospace enterprises face a similar challenge when trying to run consistent high-altitude connectivity while operating in remote locations, which can also be very expensive. This is where Project OWL comes into play: developing new technologies to help address these challenges. Formed …
Posted by Sergio Boixo and Vadim Smelyanskiy, Principal Scientists, Google Quantum AI Team A full-scale error-corrected quantum computer will be able to solve some problems that are impossible for classical computers, but building such a device is a huge endeavor. We are proud of the milestones that we have achieved toward a fully error-corrected quantum …
Read more “How to compare a noisy quantum processor to a classical computer”
Today, we’re pleased to announce the preview of Amazon SageMaker Profiler, a capability of Amazon SageMaker that provides a detailed view into the AWS compute resources provisioned during training deep learning models on SageMaker. With SageMaker Profiler, you can track all activities on CPUs and GPUs, such as CPU and GPU utilizations, kernel runs on …
Google Cloud’s Cloud Storage is home to reams of training data, models and checkpoints that you need to train and serve AI workloads, delivering the scale, performance, simplicity and cost-effectiveness that are the hallmarks of a cloud storage system. But when it comes time for an AI workload to actually access that data, it isn’t …
Read more “Cloud Storage FUSE is now optimized for GKE and AI workloads”
When it comes to hosting applications on Amazon Web Services (AWS), one of the most important decisions you will need to make is which Amazon Elastic Compute Cloud (EC2) instance type to choose. EC2 instances are virtual machines that allow you to run your applications on AWS. They come in various sizes and configurations—known as …