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Favorite Condé Nast’s editorial teams had no fast way to do multimodal video discovery. They were spending an average of 250 minutes per content discovery task, manually scrubbing through a library of more than 140,000 videos. They relied on titles and descriptions to find relevant clips. In a media environment
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Shared by AWS Machine Learning September 29, 2026
Favorite You’re a director of contracting, responsible for hundreds, maybe thousands, of vendor contracts. Each one is packed with critical data: contract values, expiration dates, signing status, and key contacts. With that information locked inside PDFs, you and your team spend hours manually extracting it, maintaining spreadsheets, and fielding the
Read More Shared by AWS Machine Learning September 29, 2026
Favorite In Part 1 of this series, we covered the foundational principles of prompt engineering in Amazon Quick: specificity, context-setting, few-shot examples, and the CRISPE framework for complex requests. Those principles apply universally. In this post, we go component by component, showing you how each Quick capability interprets prompts differently
Read More Shared by AWS Machine Learning September 29, 2026
Favorite Prompt engineering in Amazon Quick determines how accurately and reliably the platform’s AI-powered features respond to your natural-language requests. Whether you’re building custom agents, authoring automation flows, or querying data through conversational analytics, the way you structure your prompts directly shapes the quality of the output you receive. In
Read More Shared by AWS Machine Learning September 29, 2026
Favorite Watch the winning trailer from the Future Vision XPRIZE, The Gifted. View Original Source (blog.google/technology/ai/) Here.
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
Read More 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
Read More 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
Read More 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
Read More 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
Read More Shared by AWS Machine Learning September 26, 2026