Amazon SageMaker AI

Headlines · 3

1. 2h
   Qlik Answers scales grounded enterprise AI on Amazon Bedrock

   In an AWS blog post, Qlik detailed the architecture behind Qlik Answers, its enterprise question-answering product: employees ask in natural language and get sourced answers drawn from knowledge bases, live analytics apps, glossary definitions or documents, across more than 40,000 customers and 11 Regions. The system is split into entry, routing, answer, specialist-agent, conversational-analytics, retrieval (Amazon OpenSearch Service) and model-access layers; model calls go through Qlik's own LLM gateway to Amazon Bedrock, with Bedrock Guardrails applying content filtering and contextual grounding validation to every request and response, and Amazon SageMaker AI hosting models not yet available in-Region. Qlik says that since February 2026 general availability its Discovery Agent has surfaced over 100,000 discoveries, that most Qlik Cloud accounts with agentic tools enabled keep using them, and that customer Lintech International saw 75% faster responses and up to 7 hours per week returned to managers — all figures reported by Qlik itself.

   AWS Machine Learning Blog · 🔥 9
2. 1d
   Best practices for Amazon SageMaker HyperPod administration and governance

   An AWS Machine Learning Blog post explains how to administer Amazon SageMaker HyperPod clusters through Amazon SageMaker Unified Studio while preserving underlying governance controls. It lays out four layers of control — organization, project, cluster, and workload — and covers designing identity, capacity, and observability policies across them, plus a "connection contract" record for each approved project-to-cluster connection.

   AWS Machine Learning Blog · 🔥 5
3. 2d
   AWS adds SageMaker inference-optimization skill for coding agents

   AWS introduced the aws-ai-ml skill through the Agent Toolkit for AWS, giving MCP-compatible coding agents such as Kiro, Claude Code and Codex SageMaker AI inference optimization and benchmarking expertise. The skill load-tests existing endpoints and reports measured throughput, latency percentiles and concurrency, ranks instance types for models stored in S3, in SageMaker JumpStart or on Hugging Face Hub, compares two benchmark runs, and generates runnable SageMaker Python SDK v3 code. It can be installed locally via an npx command or used in a preconfigured image inside a private Amazon SageMaker Studio JupyterLab space.

   AWS Machine Learning Blog · 🔥 0

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