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​[[{“value”:” Recordings of business meetings, interviews, and customer interactions have become essential for preserving important information. However, transcribing and summarizing these recordings manually is often time-consuming and labor-intensive. With the progress in generative AI and automatic speech recognition (ASR), automated solutions have emerged to make… Read More »Build a serverless audio summarization solution with Amazon Bedrock and Whisper Kaiyin Hu AWS Machine Learning Blog

​[[{“value”:” As companies and individual users deal with constantly growing amounts of video content, the ability to perform low-effort search to retrieve videos or video segments using natural language becomes increasingly valuable. Semantic video search offers a powerful solution to this problem, so users can… Read More »Implement semantic video search using open source large vision models on Amazon SageMaker and Amazon OpenSearch Serverless Alexander Arzhanov AWS Machine Learning Blog

​[[{“value”:” GPUs are a precious resource; they are both short in supply and much more costly than traditional CPUs. They are also highly adaptable to many different use cases. Organizations building or adopting generative AI use GPUs to run simulations, run inference (both for internal… Read More »Multi-account support for Amazon SageMaker HyperPod task governance Nisha Nadkarni AWS Machine Learning Blog

​[[{“value”:” Businesses rely on precise, real-time insights to make critical decisions. However, enabling non-technical users to access proprietary or organizational data without technical expertise remains a challenge. Text-to-SQL bridges this gap by generating precise, schema-specific queries that empower faster decision-making and foster a data-driven culture.… Read More »Build a Text-to-SQL solution for data consistency in generative AI using Amazon Nova Mansi Sharma AWS Machine Learning Blog

​[[{“value”:” In a recent episode of High Signal, we spoke with Dr. Fei-Fei Li about what it really means to build human-centered AI, and where the field might be heading next. Fei-Fei doesn’t describe AI as a feature or even an industry. She calls it… Read More »What Comes After the LLM: Human-Centered AI, Spatial Intelligence, and the Future of Practice Duncan Gilchrist and Hugo Bowne-Anderson AI & ML – Radar

​[[{“value”:” This post is co-written with Qing Chen and Mark Sinclair from Radial. Radial is the largest 3PL fulfillment provider, also offering integrated payment, fraud detection, and omnichannel solutions to mid-market and enterprise brands. With over 30 years of industry expertise, Radial tailors its services… Read More »Modernize and migrate on-premises fraud detection machine learning workflows to Amazon SageMaker Jake Wen AWS Machine Learning Blog

​[[{“value”:” For an AI model to perform effectively in specialized domains, it requires access to relevant background knowledge. A customer support chat assistant, for instance, needs detailed information about the business it serves, and a legal analysis tool must draw upon a comprehensive database of… Read More »Contextual retrieval in Anthropic using Amazon Bedrock Knowledge Bases Suheel Farooq AWS Machine Learning Blog

​[[{“value”:” As organizations look to incorporate AI capabilities into their applications, large language models (LLMs) have emerged as powerful tools for natural language processing tasks. Amazon SageMaker AI provides a fully managed service for deploying these machine learning (ML) models with multiple inference options, allowing… Read More »Run small language models cost-efficiently with AWS Graviton and Amazon SageMaker AI Vincent Wang AWS Machine Learning Blog