Favorite Amazon Textract is a fully managed machine learning (ML) service that automatically extracts text, handwriting, layout elements, and structured data from scanned documents. Organizations use Amazon Textract to automate document processing workflows such as invoice processing, mortgage application intake, insurance claim handling, and identity verification, eliminating manual data entry
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Shared by AWS Machine Learning September 28, 2026
Favorite Synthetic monitoring emulates real user journeys through automated transactions. Rather than waiting for customers to encounter problems, teams continuously validate critical workflows (logins, purchases, form submissions) on a scheduled basis. With this approach, you detect problems faster when performance degrades or UI interactions break. For customer-facing businesses, especially in
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Shared by AWS Machine Learning September 28, 2026
Favorite In this post, you turn a text prompt into an image, then animate that image into a short video on Amazon SageMaker AI. You deploy two endpoints from the same AWS vLLM-Omni Deep Learning Container (DLC): a real-time endpoint for FLUX.2-klein-4B image generation and an asynchronous endpoint for Wan2.1-VACE-1.3B
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Shared by AWS Machine Learning September 28, 2026
Favorite Voice agents, interactive learning applications, accessibility tools, and customer service assistants need to respond without long silent pauses. In this tutorial, you deploy a text-to-speech (TTS) model on Amazon SageMaker AI that can start playing speech before it finishes generating the full response. You use the AWS vLLM-Omni Deep
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Shared by AWS Machine Learning September 28, 2026
Favorite When you post-train a Mixture-of-Experts (MoE) model with Reinforcement Learning from Human Feedback (RLHF) or Group Relative Policy Optimization (GRPO) at scale, three simultaneous challenges emerge. The first requires coordinating heterogeneous compute for rollout generation and policy training. Second, sustaining high-throughput communication across hundreds of accelerators. And third, dynamically
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Shared by AWS Machine Learning September 26, 2026
Favorite Any team working with spoken audio hits the same wall with generic speech-to-text. Think contact-center calls, all-hands meetings, podcasts, depositions, and broadcast media. These workloads need two things that standard transcription gets wrong. First, timestamps land at the utterance level, off by several seconds. Second, there’s no reliable answer
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Shared by AWS Machine Learning September 25, 2026
Favorite With Amazon SageMaker HyperPod and Qumulo, you can place training compute in one AWS Region and keep your dataset in another. Training large AI models requires massive GPU capacity, but your ideal compute resources and your training data don’t always reside in the same AWS Region. Accessing data across
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Shared by AWS Machine Learning September 25, 2026
Favorite This post was written with contributions from Datacor’s TrackAbout engineering and product teams. For gas and welding distributors, rental billing on assets such as cylinders and bulk tanks is a significant share of total revenue. Yet the data needed to manage those assets was often locked in disconnected systems,
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Shared by AWS Machine Learning September 25, 2026
Favorite With voice cloning, you can generate new speech in a target speaker’s voice from a short reference recording, without retraining a model. You can now deploy the publicly available Qwen3-TTS-12Hz-1.7B-Base text-to-speech model from Amazon SageMaker JumpStart to a fully managed, real-time inference endpoint. Voice cloning reproduces the vocal identity
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Shared by AWS Machine Learning September 25, 2026
Favorite Executives need to make data-driven decisions during live business reviews, where accuracy and speed matter. A conversational agentic AI assistant can meet this need by answering data questions instantly. But the stakes are high: a wrong number or a slow response in front of leadership carries immediate professional consequences,
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Shared by AWS Machine Learning September 25, 2026