A Technology Broker and Management Company Specializing in KM Systems
Favorite Coding and agentic workloads are asking more of AI models than ever: refactor a repository spanning hundreds of files, sustain a multi-hour agentic workflow without losing context, and reason through complex systems problems with tool use at every step. Meeting those demands with open-weight models has historically meant provisioning
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Shared by AWS Machine Learning October 6, 2026
Favorite Airlines already have apps and websites where travelers check flights, pick seats, and manage bookings, and adding a natural voice layer opens those tasks to spoken requests. With this voice layer, a traveler can change a seat or check a delay by speaking, without leaving the app or navigating
Read More Shared by AWS Machine Learning October 6, 2026
Favorite We recently introduced the ability to create and manage Amazon SageMaker Spaces on Amazon SageMaker HyperPod EKS clusters directly from the Amazon SageMaker Studio UI. Data scientists and machine learning (ML) engineers can now launch JupyterLab and Code Editor environments on HyperPod clusters without leaving their browser or using
Read More Shared by AWS Machine Learning October 6, 2026
Favorite Amazon SageMaker HyperPod gives machine learning (ML) teams access to large pools of accelerated compute for training and fine-tuning models. When several teams share one cluster, the technical setup is usually straightforward. The challenging part is governance. You must decide which teams can use the cluster, how much capacity
Read More Shared by AWS Machine Learning October 6, 2026
Favorite With generative AI adoption moving faster than the personal computer or the internet and global AI-related investment in 2025 representing $581.69 billion, organizations must position their workforce to use AI to power their operations while employing it responsibly. Researchers affiliated with the AI Adoption Initiative argue that the workforce
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Favorite A critical challenge that emerges as multi-agent systems move from experimentation to production is making sure that these systems are consistently helpful, accurate, and explainable in real-world scenarios. Enterprises are increasingly adopting multi-agent systems to solve complex, real-world problems that require reasoning across data sources, tools, and business constraints.
Read More Shared by AWS Machine Learning October 5, 2026
Favorite Managing access permissions effectively is an important aspect of maintaining a secure and collaborative environment in Amazon Quick. Quick supports versatile user management options designed to accommodate various identity types and organizational needs. You can provision users natively through Quick Identity or manage them through enterprise identity providers such
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Favorite When a user asks the support assistant, a Retrieval Augmented Generation (RAG) application built with LangChain to compare two products across three dimensions, they’re effectively posing six questions simultaneously. Similarity search uses a single query vector to encapsulate all the intents. The retriever then generates the best approximation of
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Favorite Amazon Quick is Amazon’s agentic AI companion built for work. You build agents that reason over your data, call action connectors, and carry multi-step tasks to completion. Promoting those resources (chat agents, action connectors, knowledge bases, flows, and spaces) from a development to a production AWS account, the way
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Favorite Engineers increasingly use coding assistance tools to accelerate their development workflows. Today, Amazon SageMaker AI optimized generative AI inference introduces the aws-ai-ml skill, available through the Agent Toolkit for AWS. This skill gives coding agents like Kiro, Claude Code, and Codex deep expertise in inference optimization and benchmarking. Install
Read More Shared by AWS Machine Learning October 5, 2026