Category: FAANG

Building out a machine learning operations (MLOps) platform in the rapidly evolving landscape of artificial intelligence (AI) and machine learning (ML) for organizations is essential for seamlessly bridging the gap between data science experimentation and deployment while meeting the requirements around model performance, security, and compliance. In order to fulfill regulatory and compliance requirements, the …

Read more “Promote pipelines in a multi-environment setup using Amazon SageMaker Model Registry, HashiCorp Terraform, GitHub, and Jenkins CI/CD”

Editorial note: Whether you’re new to Google Cloud or an experienced user, read on to learn how Duet AI can help you learn about products and services, generate code and commands, and understand your environment. In fast-moving organizations, engineers often work on large projects that utilize hundreds of cloud products and services, spanning multiple teams …

Read more “3 new ways Duet AI can help you get things done fast in the Google Cloud console”

This post is cowritten with Ming (Melvin) Qin, David Bericat and Brad Genereaux from NVIDIA. Medical imaging AI researchers and developers need a scalable, enterprise framework to build, deploy, and integrate their AI applications. AWS and NVIDIA have come together to make this vision a reality. AWS, NVIDIA, and other partners build applications and solutions …

Read more “Build a medical imaging AI inference pipeline with MONAI Deploy on AWS”

AI models continue to get bigger, requiring larger compute clusters with exa-FLOPs (10^18 FLOPs) of computing. While large-scale models continue to unlock new capabilities, driving down the cost of training and serving these models is the key to sustaining the pace of this innovation. Typically, the tensor operations (ops)1 are the most compute-intensive part of …

Read more “Introducing Accurate Quantized Training (AQT) for accelerated ML training on TPU v5e”

Posted by Xin Wang, Software Engineer, and Nishanth Dikkala, Research Scientist, Google Research Contemporary deep learning models have been remarkably successful in many domains, ranging from natural language to computer vision. Transformer neural networks (transformers) are a popular deep learning architecture that today comprise the foundation for most tasks in natural language processing and also …

Read more “Alternating updates for efficient transformers”

Posted by Xin Wang, Software Engineer, and Nishanth Dikkala, Research Scientist, Google Research Contemporary deep learning models have been remarkably successful in many domains, ranging from natural language to computer vision. Transformer neural networks (transformers) are a popular deep learning architecture that today comprise the foundation for most tasks in natural language processing and also …

Read more “Alternating updates for efficient transformers”