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Selecting a vector store for Amazon Bedrock Knowledge Bases

Favorite When building a Retrieval Augmented Generation (RAG) solution with Amazon Bedrock Knowledge Bases, selecting the right vector store impacts performance and cost. Amazon Bedrock Knowledge Bases offers a fully managed option and a customer-managed option where you choose your own vector store. This post focuses on the customer-managed path,

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Shared by AWS Machine Learning September 18, 2026

Introducing Amazon SageMaker HyperPod Inference Gateway

Favorite Eliminate GPU waste. Reduce first-token latency by up to 82%. Install one Kubernetes-native addon with zero application changes. The problem: Naive routing wastes your most expensive resource Running large language models (LLMs) at scale on GPU clusters is expensive. The default Kubernetes load balancers are making it worse. Round-robin

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Shared by AWS Machine Learning September 18, 2026

Co-creating the future of fashion with Google

Favorite Google worked side-by-side with designers Jane Wade and Sergio Hudson to custom-design Google Flow tools to prep for NYFW. View Original Source (blog.google/technology/ai/) Here.

Making global data easier to explore

Favorite Google and the UN system have launched the UN System Data Commons, a new open platform making global statistics accessible and easy to search. View Original Source (blog.google/technology/ai/) Here.

Build a serverless PII redaction pipeline with Amazon Bedrock Data Automation

Favorite Organizations that process thousands of scanned documents daily, including medical forms, insurance claims, and financial records, face a recurring compliance need: personally identifiable information (PII) redaction before documents are shared with third parties or processed downstream. Manual redaction doesn’t scale: It consumes staff hours, introduces human error, and creates

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Shared by AWS Machine Learning September 17, 2026

Fault tolerant distributed training on Amazon EKS using NVRx

Favorite Large-scale distributed training jobs run for hours or days across dozens of nodes. At that scale and duration, interruptions are statistically inevitable: network partitions, memory errors, software exceptions, or infrastructure events will eventually disrupt at least one worker. A single GPU fault triggers a cascade: NVIDIA Collective Communication Library

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Shared by AWS Machine Learning September 17, 2026

Improving HCLS AI reasoning with open-source agent skills

Favorite AI agents built on foundation models (FMs) often misapply healthcare and life sciences (HCLS) decision frameworks, even when they’ve seen the guidelines in training and in the system prompt. Ask an agent to classify a TP53 missense variant using ACMG/AMP criteria. It will cite the correct framework but misapply

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Shared by AWS Machine Learning September 17, 2026