Favorite Deploying generative AI models to production requires finding the right combination of instance type, serving container with settings, and optimization strategy. This process typically requires a long iteration cycle of optimization and manual benchmarking. In April 2026, Amazon SageMaker AI launched this inference recommendations, so customers can programmatically get
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Shared by AWS Machine Learning July 14, 2026
Favorite When you deploy generative AI agents into multi-tenant production architectures, you face a specific identity problem: when an agent calls a downstream API on behalf of a user, whose identity travels with the call? Running the call as the agent’s service identity collapses the audit trail, because every downstream
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Shared by AWS Machine Learning July 14, 2026
Favorite This post is co-written with Vijay Venkatesh, CTO at Bluesight. If you build software for hospitals, you know that compliance work scales poorly. Hospitals managing 340B Drug Pricing Program compliance face a compounding data problem. Proving that a Group Purchasing Organization (GPO) purchased drug qualifies for an exception requires
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Shared by AWS Machine Learning July 14, 2026
Favorite AI as accessibility: what happened when a neurodivergent solutions architect stopped fighting his brain and started building. In this post, I share how AI serves as an accessibility tool for neurodivergent professionals. The system is built on Amazon Quick on your desktop, an AI-powered desktop and web assistant that
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Shared by AWS Machine Learning July 14, 2026
Favorite Build with the smartest family of models from OpenAI yet, on Amazon Bedrock’s next-generation inference engine. Organizations scaling autonomous agents and AI-powered products need frontier intelligence that performs reliably across hundreds of steps, from coding agents shipping production code to cyber security research probing novel attack surfaces to genomics
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Shared by AWS Machine Learning July 14, 2026
Favorite When prefill and decode share a GPU, long prompts stall token generation for every concurrent request. Disaggregated Prefill and Decode (DPD) removes this interference by running each phase on separate GPU pools connected through Elastic Fabric Adapter (EFA) with Remote Direct Memory Access (RDMA). Large language model (LLM) inference
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Shared by AWS Machine Learning July 11, 2026
Favorite In this post, learn how KTern.AI, an SAP digital transformation platform, used Amazon Bedrock AgentCore to build and deploy AI agents ready for enterprise-scale SAP transformation workloads. These agents autonomously orchestrate workflows from reverse engineering, fit-to-standard, and code analysis to exception mining in Finance and Sales processes. The result
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Shared by AWS Machine Learning July 11, 2026
Favorite This post was co-written with Daniel Han and Michael Han from Unsloth. Deploying large foundation models (FMs) stored at their original 16-bit floating-point precision (BF16 or FP16) is expensive. They need large GPU instances, driving up serving costs, and slowing down iteration cycles. Quantization addresses this by reducing the
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Shared by AWS Machine Learning July 11, 2026
Favorite An artificial intelligence (AI) agent can process an invoice, help adjudicate a claim, or classify a support ticket in a proof of concept. But running these agents across thousands or even millions of work items in a production environment introduces an entirely different set of challenges. At enterprise scale,
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Shared by AWS Machine Learning July 11, 2026
Favorite In this post we show how to build a semantic layer on AWS using Stardog’s Semantic AI Application over Amazon Aurora and Amazon Redshift, and how to run a Strands Agents agent on Amazon Bedrock AgentCore that queries the layer to answer customer 360 questions across both sources without
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Shared by AWS Machine Learning July 11, 2026
Shared by AWS Machine Learning July 14, 2026
