AI

AgentOps: Operationalize agentic AI at scale with Amazon Bedrock AgentCore

When you build agentic AI solutions, you face unique operational challenges. Agents make unpredictable decisions, costs spiral unexpectedly, and debugging non-deterministic failures seems impossible. Agentic AI applications don’t just execute predetermined workflows. They reason, adapt, and make autonomous decisions, and DevOps practices need to be adapted. That’s where AgentOps comes in, the operational discipline for

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Accelerate LLM model loading and increase context windows with GPUDirect on Amazon FSx for Lustre and TurboQuant

If you’re iterating on deploying large language models (LLMs) on AWS GPU instances, you’ve probably noticed the larger the model to be loaded into GPU High Bandwidth Memory (HBM), the longer the painful wait until the GPUs are ready for inference. As models grow to hundreds of billions of parameters and GPU environments grow ever

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Amazon Quick integration with time-series databases for market intelligence using MCP

Model Context Protocol (MCP) integration in Amazon Quick transforms how financial analysts access time-series market intelligence, removing the need for complex database queries. As a financial analyst, you navigate millions of stock trades flowing through markets every second, searching for patterns that drive trading decisions. Financial institutions often use time series databases to analyze high-frequency

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Comprehensive observability for Amazon SageMaker AI LLM inference: From GPU utilization to LLM quality

Deploying large language models (LLMs) at scale on Amazon SageMaker AI Inference makes observability a critical pillar of any production machine learning (ML) strategy. Unlike conventional software that returns deterministic outputs, LLMs generate variable, free-form responses that are difficult to validate with standard metrics. LLM output quality can change over time as input distributions shift,

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