Tag: faang

Retrieval Augmented Generation (RAG) is a fundamental approach for building advanced generative AI applications that connect large language models (LLMs) to enterprise knowledge. However, crafting a reliable RAG pipeline is rarely a one-shot process. Teams often need to test dozens of configurations (varying chunking strategies, embedding models, retrieval techniques, and prompt designs) before arriving at …

Read more “Automate advanced agentic RAG pipeline with Amazon SageMaker AI”

In real-world video and image analysis, businesses often face the challenge of detecting objects that weren’t part of a model’s original training set. This becomes especially difficult in dynamic environments where new, unknown, or user-defined objects frequently appear. For example, media publishers might want to track emerging brands or products in user-generated content; advertisers need …

Read more “Enhance video understanding with Amazon Bedrock Data Automation and open-set object detection”

This post was co-authored with Jingwei Zuo from TII. We are excited to announce the availability of the Technology Innovation Institute (TII)’s Falcon-H1 models on Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, developers and data scientists can now use six instruction-tuned Falcon-H1 models (0.5B, 1.5B, 1.5B-Deep, 3B, 7B, and 34B) on AWS, and have access …

Read more “TII Falcon-H1 models now available on Amazon Bedrock Marketplace and Amazon SageMaker JumpStart”

At Google Cloud Next 2025, we announced new inference capabilities with GKE Inference Gateway, including support for vLLM on TPUs, Ironwood TPUs, and Anywhere Cache.  Our inference solution is based on AI Hypercomputer, a system built on our experience running models like Gemini and Veo 3, which serve over 980 trillion tokens a month to …

Read more “Scaling high-performance inference cost-effectively”

As generative AI continues to transform how enterprises operate—and develop net new innovations—the infrastructure demands for training and deploying AI models have grown exponentially. Traditional infrastructure approaches are struggling to keep pace with today’s computational requirements, network demands, and resilience needs of modern AI workloads. At AWS, we’re also seeing a transformation across the technology …

Read more “Powering innovation at scale: How AWS is tackling AI infrastructure challenges”

We are excited to announce the general availability of fine-grained compute and memory quota allocation with HyperPod task governance. With this capability, customers can optimize Amazon SageMaker HyperPod cluster utilization on Amazon Elastic Kubernetes Service (Amazon EKS), distribute fair usage, and support efficient resource allocation across different teams or projects. For more information, see HyperPod task governance best …

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This post was written with Mohamed Hossam of Brightskies. Research universities engaged in large-scale AI and high-performance computing (HPC) often face significant infrastructure challenges that impede innovation and delay research outcomes. Traditional on-premises HPC clusters come with long GPU procurement cycles, rigid scaling limits, and complex maintenance requirements. These obstacles restrict researchers’ ability to iterate …

Read more “Accelerating HPC and AI research in universities with Amazon SageMaker HyperPod”