AWS Machine Learning Blog Amazon SageMaker is a fully managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning (ML) models at scale. SageMaker makes it easy to deploy models into production directly through API calls to the service. Models are packaged into containers for robust and […]Continue reading
AWS Machine Learning Blog Amazon SageMaker is a fully managed service that enables developers and data scientists to quickly and effortlessly build, train, and deploy machine learning (ML) models at any scale. SageMaker makes it straightforward to deploy models into production directly through API calls to the service. Models are packaged into containers for robust […]Continue reading
AWS Machine Learning Blog Amazon SageMaker makes it straightforward to deploy machine learning (ML) models for real-time inference and offers a broad selection of ML instances spanning CPUs and accelerators such as AWS Inferentia. As a fully managed service, you can scale your model deployments, minimize inference costs, and manage your models more effectively in […]Continue reading
AWS Machine Learning Blog Amazon SageMaker Studio provides a fully managed solution for data scientists to interactively build, train, and deploy machine learning (ML) models. Amazon SageMaker notebook jobs allow data scientists to run their notebooks on demand or on a schedule with a few clicks in SageMaker Studio. With this launch, you can programmatically […]Continue reading
AWS Machine Learning Blog Amazon Elastic Compute Cloud (Amazon EC2) accelerated computing portfolio offers the broadest choice of accelerators to power your artificial intelligence (AI), machine learning (ML), graphics, and high performance computing (HPC) workloads. We are excited to announce the expansion of this portfolio with three new instances featuring the latest NVIDIA GPUs: Amazon […]Continue reading
AWS Machine Learning Blog Today, Amazon SageMaker launches a new version (0.25.0) of Large Model Inference (LMI) Deep Learning Containers (DLCs) and adds support for NVIDIA’s TensorRT-LLM Library. With these upgrades, you can effortlessly access state-of-the-art tooling to optimize large language models (LLMs) on SageMaker and achieve price-performance benefits – Amazon SageMaker LMI TensorRT-LLM DLC […]Continue reading
AWS Machine Learning Blog Amazon Personalize is excited to announce the new Next Best Action (aws-next-best-action) recipe to help you determine the best actions to suggest to your individual users that will enable you to increase brand loyalty and conversion. Amazon Personalize is a fully managed machine learning (ML) service that makes it effortless for […]Continue reading
AWS Machine Learning Blog Amazon SageMaker Canvas is a rich, no-code Machine Learning (ML) and Generative AI workspace that has allowed customers all over the world to more easily adopt ML technologies to solve old and new challenges thanks to its visual, no-code interface. It does so by covering the ML workflow end-to-end: whether you’re […]Continue reading
AWS Machine Learning Blog This is a guest post by A.K Roy from Qualcomm AI. Amazon Elastic Compute Cloud (Amazon EC2) DL2q instances, powered by Qualcomm AI 100 Standard accelerators, can be used to cost-efficiently deploy deep learning (DL) workloads in the cloud. They can also be used to develop and validate performance and accuracy […]Continue reading
AWS Machine Learning Blog Amazon Textract is a machine learning (ML) service that automatically extracts text, handwriting, and data from any document or image. AnalyzeDocument Layout is a new feature that allows customers to automatically extract layout elements such as paragraphs, titles, subtitles, headers, footers, and more from documents. Layout extends Amazon Textract’s word and […]Continue reading
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